research article a novel method for acquiring engineering...

Research ArticleA Novel Method for Acquiring Engineering-OrientedOperational Empirical Knowledge

Lijun Liu Zuhua Jiang Bo Song Hongyuan Zhu and Xinyu Li

Department of Industrial Engineering Shanghai Jiao Tong University 800 Dongchuan Road Minhang DistrictShanghai 200240 China

Correspondence should be addressed to Zuhua Jiang zhjiangsjtueducn

Received 26 September 2015 Revised 26 January 2016 Accepted 16 February 2016

Academic Editor Vincent Hilaire

Copyright copy 2016 Lijun Liu et alThis is an open access article distributed under the Creative CommonsAttribution License whichpermits unrestricted use distribution and reproduction in any medium provided the original work is properly cited

The operational knowledge of skilled technicians gained from years of experience is invaluable for an enterprise Possession ofsuch knowledge will facilitate an enterprise sharing technicianrsquos know-how and training of new employees effectively Howeveruntil now there is rare efficient quantitative method to obtain this kind of tacit knowledge In this paper we propose a concept ofengineering-oriented operational empirical knowledge (OEK) to describe this kind of knowledge anddesign a framework to acquireOEK from skilled technicianrsquos operations The framework integrates motion analysis motion elicitation and intent analysis Themodular arrangement of predetermined time standards (MODAPTS) is used to divide the technicianrsquos operational process intobasic motion elements and the variable precision rough set (VPRS) algorithm is used to extract the technicianrsquos OEK contentwhich combined with the technicianrsquos intent elicited via interview the completed OEK is obtained At the end of our study anengineering case is used to validate the feasibility of the proposed method which shows that satisfactory results have been reachedfor the study

1 Introduction

Years of experience in an operational work can bring atechnician much know-how for accomplishing the tasks effi-ciently The operational know-how of the skilled technicianis of great importance for an enterprise which often hasnew workers to train and need to update the workflow toincrease productivity [1 2] With the operational know-howtransferred to novice the novice can improve his opera-tional skills and then increase manufacturing productivityand product quality However the operational know-howhas not been sufficiently harnessed due to the lack of anefficient knowledge acquisition method [3ndash5] Therefore itis imperative to develop an efficient acquisition method forsuch knowledge acquisition

Operational know-how is regarded as a kind of tacitknowledge which is hard to formalize communicate andshare with others [6] The transformation from tacit knowl-edge to explicit knowledge is a challenging task Moreovermany of tacit knowledge acquisition approaches are based onthe literal documents for the documents might be suitable

for explanation of procedure However it is very rare toexplain the secret of the technical operation which the skilledtechnicians haveThe acquisition of operational know-how ismore difficult compared to the acquisition of tacit knowledgebased on the literal documents

In the existing literatures apprenticeship and observa-tion are the most useful methods to acquire operationalknow-how [3] Apprenticeship is formal arrangements wherespecialized knowledge or skill from a domain expert ispassed to a novice The novice can acquire the same levelof competence as the expert after extensively practicing theskill for a prescribed period of time [7] Observation isalso recognized as a valuable form of acquiring operationalknowledge [8] By observing the actions of an expert theobserver gains insights into expertrsquos practices and builds hispersonal knowledge base For example in order to obtainan experienced bakerrsquos operating technique the employeesof Panasonic worked together with the skilled baker for along time observing and imitating the bakerrsquos motion Even-tually the employees obtained the bakerrsquos operational know-how However apprenticeship and observation belong to the

Hindawi Publishing CorporationMathematical Problems in EngineeringVolume 2016 Article ID 9754298 19 pageshttpdxdoiorg10115520169754298

2 Mathematical Problems in Engineering

qualitative methods The disadvantages of these methodsare obvious they are inefficient time-consuming and notreplicable for different acquisition results will occur due todifferent cognition and learning modes of observers There-fore development of an efficient and quantitative method toelicit operational know-how becomes an emergent task in thefield of knowledge management

Aiming at the obstacles in the field of engineering-oriented operational know-how acquisition we proposea concept of engineering-oriented operational empiricalknowledge (OEK) to describe this kind of knowledge andthen present an explicit definition and the representationof engineering-oriented OEK A novel OEK acquisition(OEKA) framework is provided in our study which cancomplete the transformation from technicianrsquos operationalmotion to the structured OEKThe OEKA integrates motionanalysis motion elicitation and intent analysis First theworkerrsquos specific operation process is divided into basicmotion elements by means of modular arrangement of pre-determined time standards (MODAPTS) which is a systemused to analyze the performance of manual work [9] Secondwe combine the motion elements with scenario factors toestablish the scenario model and then the algorithm ofvariable precision rough set (VPRS) is used to extract thecritical motion of skilled technicians Third the skilledtechnicianrsquos intent behind theOEK content is elicited with theaid of interview At last the structured representation of OEKis constructed in the postprocessing

Our contribution in this work is threefold

(1) We define the concept of engineering-oriented OEKand provide the structured representation of OEK

(2) We propose an integrated OEK acquisition frame-work integrating motion analysis motion elicitationand intent analysis by which empirical knowledge inthe engineerrsquos operation can be obtained

(3) We demonstrate that the extracted OEK can capturethe technical know-how and can be used for novicesrsquoskill training

The rest of the paper is organized as follows we firstreview the related works in Section 2 In Section 3 we definethe concept of OEK and then provide the representationmodel of OEK In Section 4 we present the OEK acquisitionmethod and its advantage Section 5 describes the completeprocess of OEK acquisition Section 6 provides a detailedcase study The comparison of our method with some relatedworks is discussed in Section 7 Conclusions and futurestudies are outlined in the last section

2 Related Works

21 Tacit Knowledge and Tacit Knowledge AcquisitionKnowledge tied to the senses physical experiences move-ment skills intuition unarticulated mental models orimplicit rules of thumb is tacit [18 19] Tacit knowledge isrooted in the action procedures routines ideals commit-ment and emotion which makes it differ from the explicitknowledge that can be uttered and obtained as drawing and

writing Individuals with tacit knowledge are often unawareof what they possess and how valuable they are for others

Tacit knowledge acquisition means eliciting tacit knowl-edge from various knowledge sources and representing it inthe right form in order to share the knowledge among orga-nizational members and reuse it [20 21] Up till now manytacit knowledge extractionmethods have been developed andapplied in the fields of software engineering manufacturingengineering and health care [22 23] Many of these methodsare based on literal documents And with the developmentof natural language processing machine learning the tacitknowledge acquisitionmethods based on document aremoreand more efficient and valuable

Although the methods based on literal document aresuitable for acquiring the explanation of procedures theyare difficult to acquire the essence of the operational know-how [24] In addition traditional knowledge extractiontechniques work well in eliciting tacit knowledge in literaldocuments They do not elicit the intent of the documentsHowever intent is an indispensable part of tacit knowledgethe intent elicitation should be integrated in the tacit knowl-edge acquisition method [25]

22 Operational Know-How and Knowledge AcquisitionOperational know-how is regarded as a kind of tacit knowl-edge [5] Such knowledge does not originate from theliterature or documents identified as explicit knowledgebut originates from the experience of an individual [26]Consequently it is difficult to be expressed and transferred[27 28]

Many scholars have committed to obtaining the opera-tional know-how and proposed many acquisition models ormethods In Table 1 the existing literatures about operationalknow-how acquisition are analyzed from the perspectivesof method description qualitative or quantitative object ofextraction applied technology application field and draw-back

These methods are divided into three types qualitativemethod quantitative method and mixed method

Observation and apprenticeship are classic qualitativemethods for extracting the operational know-how of skilledtechnicians [3] Observation is recognized as a valuable formof learning according to the social cognitive theory [8] Theobserver gains insights into the skilled techniciansrsquo practicesand builds his personal knowledge base by observing theactions of the expert [29] Apprenticeship is formal arrange-ments where specialized knowledge or skill from a domainexpert is passed to a novice The novice will acquire thesame level of competence as the expert after extensivelypracticing the skill for a prescribed period of time [7] Thesequalitative acquisition methods are successful at transferringthe operational know-how from experts to novices and arethe focus of earlier literatures However these methods haveobvious drawbacks such as the time-consuming and theinconsistent acquisition results for the different cognition andlearning modes of learners

The quantitativemethod of operational know-how acqui-sition is another type in which the specific algorithm ortool is used to elicit the operational know-how Nakagawa

Mathematical Problems in Engineering 3

Table 1 Related literaturesrsquo analysis

Literature Methoddescription

Qualitative orquantitative

Object ofextraction

Appliedtechnology Application field Drawback

Bandura [8] andNonaka [10] Observation Qualitative

methodActions of

domain expert Observe Manual workInconsistentacquisitionresults

Collis et al [7 11] Apprenticeship Qualitativemethod

Specializedknowledge orskill of domain

expert

Extensivelypracticing the

skillNot limited

Time-consuming andinconsistentacquisitionresults

Hashimoto et al[12] and Yoshida etal [5]

Personal motionsensing Mixed method

Skilledoperations of

skilledtechnicians

Field-orientedinterview

human motioncapture andvideo analysis

Manufacturingindustry

Depends on theinterviewseriously

Watanuki [13] Method of skillstransfer

Qualitativemethod

Castingtechnologiesand skills of

skilledtechnicians

Sharing a ldquobardquo(place) usingmultimedia

technology andvirtual realitytechnology

Manufacturingskills training

Therepresentationof knowledge is

vague

Nakagawa et al[14] and Huang etal [15]

Skill educationservice

Quantitativemethod

Extract skillsfrom nursing

motion (focusedon bed caremotion)

Nursing skill isextracted frominterview andvideo analysisThe extractedskills areassessed

quantitatively

Nurse and careworker

It is difficult toformally

represent suchknowledge

Li et al [16] andNguyen and Zhang[2]

Mobility andphysical activity

sensingMixed method

Nursesfirefighterswaiters

housekeepersor janitors

Extracthigh-level

knowledge rulesfrom low levelsensor signalsby means of anunsupervisedapproach

Service

Aiming at theactivities of

person lackingdetailed motion

Mitsuhashi et al[17]

Motion curvedsurface analysisand composite

Quantitativemethod Skill succession

Track the wholejoint motion by

means ofmotion curvedsurfaces andconfirm the

validity of skillsuccession by

skeleton motionmovie and

curved surface

Sports andentertainment

motion

Lackingaccurate

description ofthe motionsequences

et al [14] extracted nursing skills by analyzing the bodyfoot and muscle activities of experts and nonexperts andthen developed a novel skill education service for nursingMitsuhashi et al [17] tracked the whole joint motion bymeans of motion curved surfaces and confirmed the validityof skill succession by skeleton motion movie and curvedsurface This method was used to elicit the know-how ofsports and entertainment motion The quantitative methodcan analyze the motion and behavior of person accurately

However the empirical knowledge representation in themethod is somewhat weak

The mixed method is an integrated framework includingqualitative and quantitative methods Yoshida et al [5]presented a mixed method to extract the tacit knowledgeconsisting of the skilled operations of skilled technicians inthe manufacturing industry The method comprises threeaspects the field-oriented interview the human motion cap-ture and the video analysis However this method depends


(a) Motion combanation (b) Motion sequence

Figure 1 Examples of motion combanition and motion sequence

on the interview seriously and may reduce the effectivenessof knowledge acquisition Nguyen and Zhang [2] extractedhigh-level knowledge rules from low level sensor signals bymeans of an unsupervised approach in the service Theirstudy focused on the activities of person and lacked explicitmotion descriptionThemixed method integrates the advan-tages of qualitative and quantitative methods and may besuitable to obtain the operational know-how of experts

In summary the explicit definition and representationof engineering-oriented operational knowledge are absent inthe existing literatures Such reasons have limited the devel-opment of standardized and effective knowledge acquisitionapproach Our study aims to overcome these obstacles andpresent a novel and useful knowledge acquisition frameworkby means of mixed method In the next section the formaldefinition and representation of OEK are introduced

3 Definition and Representation of OEK

31 Definition of OEK In this paper the engineering-oriented OEK is defined as follows

Engineering-oriented OEK is the operational know-how comprising the specific motion combination motionsequence formed during the repeated practice and thinkingof an individual which are of value for bettering the produc-tion process

In the manufacturing process some manual operationsare very simple For example in the drilling operation anoperator only repeats the simple motion combination ldquopulland pushrdquo as shown in Figure 1(a) In other cases theoperation can be more complex For example in the moldingoperation shown in Figure 1(b) the operator uses his lefthand to grasp a number of parts placing them into a moldand rowing them against the mold while his right handoperates the machine When the molding is finished theoperator removes the completed artifact Such a complexmotion sequence includes a series of motion elements

32 Interpretation of OEK Definition Some key concepts inthe definition of OEK are explained as follows

(1) Operational Know-How Knowledge Operational know-how knowledge is the special knowledge that facilitates

efficient engineering operationsThis kind of knowledge doesnot originate from the literature or documents but originatesfrom skilled techniciansrsquo experience [26]

(2) Motion Combination Motion combination is the integra-tion of a series of basic operational motion elements withoutpause Motion combination is a basic form of the content ofOEK This kind of OEK comes from an individualrsquos repeatedpractice and thinking which are the most effective andconvenient motions for the specific operation For examplea proficient welding operator would use some particularmotion combination in the welding process which enablesthe operator to finish the production with high quality andquick speed

The motion combination is formalized as follows

119872119888= 11989811198982sdot sdot sdot 119898119899 (1)

where119872119888denotes the motion combination and119898

119899is a basic

motion element

(3) Motion Sequence A motion sequence is an ordered setof several motion combinations where the pause betweenmotion combinations is permitted As another form of thecontent of OEK the motion sequence represents a completeoperationmethod withwhich a skilled operator conducts themost effective operation process

The motion sequence is formalized as follows

119872119904= 11989811988611198981198862sdot sdot sdot 119898119886119895 or 119898

11988711198981198872sdot sdot sdot 119898119887119896 or sdot sdot sdot

or 11989811988911198981198892sdot sdot sdot 119898119889119897

(2)

where119872119904denotes the motion sequence 119898

119894119899119894 = 119886 119887 119889

is a basic motion element and 11989811988611198981198862sdot sdot sdot 119898119886119895 is a motion

combination

33 Representation of Engineering-OrientedOEK Knowledgeacquisition is closely related with knowledge representationfor the manipulation of acquired knowledge largely dependson how the knowledge is represented to reflect domainexpertsrsquo mental model and problem solving process [30]The main body of OEK representation is OEK contentincorporating the motion combination andmotion sequence


OEK sources identification

Human motion analysis

Scenario model establishment

Obtaining initial OEK

Structured OEK

Specific operation process

MODAPTS

Start

End

Variable precision rough set

Video analysis

Interview analysis

Motion modeling

Motion eliciting

Intent eliciting

Acquisition toolAcquisition processAcquisition phase

Intent analysis

Postprocessing

OEK sources

Figure 2 Multistage OEK acquisition framework

obtained from a skilled technician Besides this since OEKis related with the specific operation process and problemscenarios it is indispensable to integrate the scenarios intoOEK representation [22 23] The operatorrsquos intent assumesa driving role of the motions so it should be consideredin the representation of OEK Moreover the OEK obtainedfrom an individual experience is not always reliable so acredibility score should be given to indicate the reliability ofOEK Taking the above factors into account a complete OEKrepresentation should include the problem scenarios con-tributorrsquos information core content intent and creditabilityas shown in the following formula

⟨OEK⟩ = ⟨problem scenarios contributor content

intent creditability⟩ (3)

4 Methodology

In many cases skilled technicians are not aware of theOEK they possess and how this knowledge can be sharedwith others In the problem solving the technicianrsquos OEKis activated by the factors of problem context to realize thespecific intent In our study not only the know-how in themotion but also the intent behind the specific motion willbe elicited The completed OEK is not obtained until theintents matching the specific actions are elicited because it

is difficult to finish the OEK elicitation process from theoperatorrsquos practical action to the structured OEK in onlyone step we present a novel multistage OEK acquisitionframework as shown in Figure 2 The framework consistsof five phases which are (1) the stage of OEK sourcesidentification (2) the stage of motion analysis (3) the stageof motion eliciting (4) the stage of intent analysis and (5)the stage of postprocessing

The following knowledge acquisition techniques are usedin the framework humanmotion analysismotion elicitationand standard interview In the previous literatures each ofthem has been used separately However we construct anew data fusion analysis method which combines the threeeffectively and overcomes each onersquos weak point

In the stage of OEK sources identification the rightmanufacturing process or engineering problem is selected asthe source of OEK acquisition

In the stage of motion analysis an engineerrsquos operation inspecific manufacturing process is captured and modeled Atfirst an engineering problem in the manufacturing process isselected as theOEK source And then the operatorrsquos operationis recorded by the video At last the motion and scenariomodel are established by means of MODAPTS [31]

The target of the stage of motion elicitation is to obtainthe OEK content The variable precision rough set algorithmis used to elicit such knowledge


In the stage of intent analysis we use the standard inter-view to recognize the real intention of an expertrsquos motionTheexpertrsquos motion matching the right intent will be retained

In the postprocessing stage the problem scenarios con-tributorrsquos information and knowledge credibility are inte-grated with theOEK contentThe structuredOEK is acquiredand the OEK acquisition process is finished

5 Description of OEK Acquisition Method

51 Identification of OEK Sources The aim of eliciting theOEK is to extract and store the skilled operatorrsquos know-how and train the novice operators The complex opera-tional process difficult to grasp becomes the appropriatecandidate of OEK sources The skilled techniciansrsquo OEKin such process will be used to support novice trainingMoreover the workflows needing to be optimized to increasethe productivity are also the potential candidate of OEKsources The expertsrsquo OEK will be valuable to aid workflowsredesign and optimization

52 Motion Analysis and Modeling The aim of this phase isto decompose the operation of operators into basic motionelements and then establish the motion and scenario model

The operation is difficult to be accurately representedand quantitatively analyzed by direct observation [24] Thespecific motion analysis tool is exploited to describe andexplain the workerrsquos operation [9] The result of motionanalysis can be used as the foundation of motion elicitationin the next stage

There are four steps in this phase

(1)Motion CaptureThemotion capture is based on the stereovision technique It is introduced not only to acquire the timeseries posture data of engineers operating some equipment ormachines but also to obtain the time varying relative positionbetween the hand position and operated equipment [5]

In this step firstly stereo vision cameras are set onthe appropriate place and calibrated Secondly the motionscapture software on the PC to record the real operation of theoperator At the same time shooting the operator by the videocamera is started It is important for video camera to take aposition that does not disturb motion capture and cover thewhole body of the expert engineer

(2) Motion Modeling In this step the human motion isbroken down into the basic motion elements based on thevideo shooting the operators In production managementfield the methods of motion analysis include therbligsMTM (methods-time measurement) [32] MOST (Maynardoperation sequence technique) [33] and MODAPTS (Themodular arrangement of predetermined time standards)

The MODAPTS is a method used to analyze the perfor-mance of manual work [31] In this paper the MODAPTSis selected to conduct the motion analysis and modelingdue to its advantages over others First MODAPTS namedldquothe language of workrdquo is a useful tool of motion analysiswhich can analyze the body motions required in a worktask and the time those motions require in standard motion

representation [34] Second MODAPTS is a simple logiceffective and low-cost motion analysis method and can beused with the aid of a desktop computer [35]

The MODAPTS has two distinguishing features elementmotion classes expressed in alphabetic form and timevalues expressed in units called ldquoMODSrdquo The movementclass denoted as ldquoMoverdquo (M) comprises motions done bybody parts such as the finger hand or arm The ldquoterminalrdquoclass comprising motions done at the end of an activityconsists of two kinds of activities Get (G) and Put (P) Theldquoauxiliaryrdquo class comprises motions that are not performedusing body parts such as walking sitting standing decidingand inspecting [36]

(3) Scenarios Modeling In the field of knowledge man-agement and knowledge engineering constructing scenariomodels will facilitate knowledge acquisition and reuse [37]Scenarios model can be used to describe the complexityof engineering problem solving process and is importantfoundation of knowledge sharing and reuse [38] Scenariosusually have characteristic elements such as the presupposedsetting agents or actors goals or objectives and sequences ofactions and events [39]

In our study the scenarios model integrates processinggoals and their subgoals and agents and their actions Inthe scenarios model agents and their actions representingthe operatorsrsquo motion combination or motion sequence arethe essential elements and are used for eliciting skillfuloperatorsrsquo OEK content during the next stage The goal andsubgoals in the model describe the different goalsrsquo level ofprocess

In the manufacturing process the factors of ldquoman-machine interactionrdquo (scenario factors for short) shouldbe considered in the scenarios model The man-machineinteraction explains the relationship between operator andmachine The special way of interaction between operatorsand machines or tools may be the potential technical know-how of skillful operators for example the choice of toolsand the preparation before processing These factors do notbelong to the motion combination or motion sequence andthen are thorny to be described in the motion model Thesefactors are added to the scenario model

The integrated scenarios model is shown in Figure 3 Themodel includes goals and subgoals of operation agents andtheir actions and story line Specifically the goal and subgoalsdescribe the detailed production processThe agents and theiractions represent the technicianrsquos operation which can beused to elicit technicianrsquos OEK The story line presents thesequence of operation such information is often provided inthe operation instructions

(4) Establishing the Integrated Model The information ofproduct quality is added to the scenario model in this stepAnd then the integrated model is established as shown inFigure 4

Up till now the completed information including oper-ational process and result is established in the integratedmodel which can be used as the fundament of motionelicitation in the next stage


Action A 1

Action 3

Action n

Action 2

Start

End

Agent A

Action 1

Agent B SystemStory line Agent N

Action A 2

Processed objects

Action B 1

Action MessageactionAgent

Action A 3

Object 1

Object nAction B 3

ldquoGoa

lrdquoSp

ecifi

c con

tent

of g

oal

Spec

ific c

onte

nt o

f go

alSp

ecifi

c con

tent

of

goal

ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo

Spec

ific c

onte

nt

of g

oal

ldquoSub

goalrdquo

Spec

ific c

onte

nt

of g

oal

ldquoSub

goalrdquo

Spec

ific c

onte

nt o

f go

al

Subg

oal 1

Subg

oal 2

Subg

oal n

Object 2

Action A 3

Action A 3

Action A 3

Action B 1

Action A 3

Action B 3

Action B 3

Action B 3

Action B 3

Action N 1

Action N 3

Action N 1

Action N 3

Action N 3

Action N 3

Action N 3

Factor A 1 Factor B 1 Factor C 1

ldquoSce

nario

fact

orsrdquo

Spec

ific f

acto

rs


middot middot middot

Figure 3 The scenario model

53 Motion Elicitation Motion elicitation is the critical stagein the OEK acquisition framework The aim of motion elic-itation is to obtain the expertrsquos critical motion and scenariofactors affecting product qualityThe variable precision roughset (VPRS) is a useful tool of knowledge acquisition and isemployed to elicit OEK in our study [40 41]

As a mathematical methodology rough sets provide apowerful tool for data analysis and knowledge discoveryfrom imprecise and ambiguous data [42] The rough setstheory has been successfully applied in diverse areas suchas knowledge acquisition forecasting modeling and datamining The variable precision rough set (VPRS) model is anextension of the original rough set model The main idea ofVPRS is to allow objects to be classified with an error smallerthan a certain predefined level [43]

531 Some Definitions Related to VPRS VPRS operates onwhat may be described as a knowledge representation systemor information system [22 23 44] An information system isa 4-tuple 119878 = (119880 119860 119881 119891) where

119880 is a nonempty finite set of objects119860 is a nonempty finite set of attributes we have 119860 =

119862 cup 119863 and 119862 cap 119863 = 120593 where 119862 is a nonempty setof condition attributes and 119863 is a nonempty set ofdecision attributes119881 is the union of attribute domains that is 119881 =

cup119886isin119860119881119886 where 119881

119886is a finite attribute domain and the

elements of 119881119886are called value of attribute 119886

119891 119880 times 119860 rarr 119881 is an information function whichassociates a unique value of each attribute with eachobject belonging to 119880 that is forall119886 isin 119860 and forall119909 isin 119880119891(119909 119886) isin 119881

119886

Suppose that information system 119878 = (119880 119860 119881 119891) witheach subset 119885 sube 119880 and an equivalence relation 119877 referredto as an indiscernibility relation corresponds to partition-ing of 119880 into a collection of equivalence classes 119880119877 =

1198641 1198642 119864

119899 which represents that 119880 could be divided

into 119899 subsets of 1198641 1198642 119864

119899according to equivalence

relation 119877 We will assume that all sets under consideration


Action A 1

Action 3

Action n

Action 2

Start

End

Agent A

Action 1


Action A 2

Processed objects

Action B 1

Action A 3

Object 1

Object nAction B 3

ldquoGoa

lrdquoSp

ecifi

c con

tent

of g

oal

ldquoSub

goalrdquo

Spec

ific c

onte

nt o

f go

al

ldquoSub

goalrdquo

Spec

ific c

onte

nt o

f go

al

ldquoSub

goalrdquo

Spec

ific c

onte

nt

of g

oal

ldquoSub

goalrdquo

Spec

ific c

onte

nt

of g

oal

ldquoSub

goalrdquo

Spec

ific c

onte

nt o

f go

al

Subg

oal 1

Su

bgoa

l 2

Subg

oal n

Object 2

Action A 3

Action A 3

Action A 3

Action B 1

Action A 3

Action B 3

Action B 3

Action B 3

Action B 3

Action N 1

Action N 3

Action N 1

Action N 3

Action N 3

Action N 3

Action N 3


ldquoSce

nario

fact

orsrdquo

Spec

ific f

acto

rs


ldquoPro

duct

qua

lityrdquo

Spec

ific f

acto

rs

Parameter A 1

Parameter A 2

Parameter A n

Parameter C 1

Parameter C 2

Parameter C n

Parameter B 1

Parameter B 2

Parameter B n


ActionAgent

Messageaction

Figure 4 The integrated model

are finite and nonempty The variable precision rough setsapproach to data analysis hinges on two basic conceptsnamely the 120573-lower and the 120573-upper approximations of aset The 120573-lower and the 120573-upper approximations can also bepresented in an equivalent form as shown belowThe 120573-lowerapproximation of the set 119885 sube 119880 and 119875 sube 119862 is

119862120573(119863) = ⋃

1minus119875119903(119885|119909119894)le120573

119909119894isin 119864 (119875) (4)

The 120573-upper approximation of the set 119885 sube 119880 and 119875 sube 119862is

119862120573(119863) = ⋃

1minus119875119903(119885|119909119894)lt1minus120573

119909119894isin 119864 (119875) (5)

where 119864(sdot) denotes a set of equivalence classes (in the abovedefinitions they are condition classes based on a subset ofattributes 119875) Consider

119885 sub 119864 (119863)

119875119903(119885 | 119909

119894) =

Card (119885 cap 119909119894)

Card (119883119894)

(6)

Based on Ziarko [43] the measure of quality of classifica-tion for the VPRS model is defined as

120574 (119875119863 120573) =

Card (⋃1minus119875119903(119885|119909119894)

119909119894isin 119864 (119875))

Card (119880) (7)


where 119885 sube 119864(119863) and 119875 sube 119862 for a specified value of 120573The value 120574(119875119863 120573) measures the proportion of objects inthe universe (119880) for which a classification (based on decisionattributes119863) is possible at the specified value of 120573

532 VPRS Knowledge Extraction Algorithm The LEM2algorithm proposed by Grzymala-Busse [45] is one of themost widely used algorithms and many real-world applica-tions use this algorithm to extract knowledge The VPRSknowledge extraction algorithm (VPRS-KE) originated fromLEM2 and took the inclusion errors into account [46] Inour study VPRS-KE is used to elicit the specific motion andscenario factors in the technicianrsquos operation

In the VPRS-KE a heuristic strategy is used in thealgorithm to extract a minimum set of rules All of positiveregions and boundary region subsets can be denoted by 119884for each 119870 isin 119884 (the decision connecting with region 119870 is119863) Given that 119862 = 119888

1and 1198882and sdot sdot sdot and 119888

119899is the conjunction

of 119899 elementary conditions the objects in the decision tablecovered by 119862 can be expressed as follows

[119862] is the cover of rule 119862 on the decision table[119862]+

119870= [119862] cap 119870 is the positive cover of 119862 on119870

[119862]minus

119870= [119862] cap (119880 minus119870) is the negative cover of 119862 on119870

A decision rule is denoted by 119903The procedure of the rule extraction is summarized in

VPRS knowledge extraction algorithm as follows

Input119870 each of the positive regions or the boundaryregions subset in 119884 (119870 isin 119884) 120573 is the allowedinclusion error for VPRSOutput 119877 set of rules extracted from region119870Step 1 119866 larr 119870 119877 larr Φ

Step 2 While 119866 = Φ

Step 3 BeginStep 4 Initialize the condition set of a rule with anempty set 119862 larr Φ

Step 5 Initialize 119862Current with all the attributes andtheir values in 119866

119862Current larr997888 119888 [119888] cap 119866 = Φ (8)

Step 6 Iteratively add conditions to 119862 until set [119862] isincluded in the set119870with an inclusion error threshold120573

While (119862 = 120593) Or (119890([119862] 119870) le 120573) doBeginSelect 119886 isin 119862Current such that card([119886] cap 119866) ismaximumIf ties occur select 119886with the smallest card([119886])and if further ties occur select the first 119886 fromthe listAdd condition 119886 to 119862

119862 larr997888 119862 cup 119886 (9)

Eliminate the conditions which have beenalready used


Update 119862Current

119862Current larr997888 119862Current minus 119862 (11)

End

Step 7 Delete the redundant conditions

For each 119886 isin 119862 doIf 119890([119862 minus 119886] 119870) le 120573

Then 119862 larr997888 119862 minus 119886 (12)

End For

Step 8 Create a rule 119903 based on 119862 and put the ruleinto 119877

119877 larr997888 119877 cup 119903 (13)

Step 9 Remove the objects covered by 119877 from 119866

119866 larr997888 119866 minus ⋃

119903isin119877

[119903] (14)

Step 10 EndStep 11 Delete the redundant rules

For each 119903 isin 119877 do

If 119870 sube ⋃

119878isin(119877minus119903)

[119904] Then 119877 larr997888 119877 minus 119903 (15)

End For

Step 12 Output 119877

54 Intent Recognition In cognitive psychology intent refersto the thoughts one has before producing an action [47]Intent recognition is to identify the goal of observed sequenceofmotions and is performed by the action agent It is a processby which an agent can gain access to the goals of another andpredict its future actions and trajectories The understandingof human intent based on human motions remains a highlyrelevant and challenging research topic [48]

In our study the standard interview is employed todetect the underlying intent behind humanmotion First theskilled operators are interviewed to evaluate OEK contentand explain the specific intent behind the motion Secondthe advantage of skillful operations and the disadvantage ofunskillful operations will be recognized

55 Postprocessing In the postprocessing the related fac-tors of problem scenarios contributors and creditabilityare integrated into the OEK content and the completedrepresentation of OEK is constructed


Table 2 MODAPTS representation of operatorrsquos welding process fragment

Number Motion of left hand Motion of right handDiscomposed

motion descriptionMOD

representationDiscomposed


representation1 Grasp the pipe M4G1

2 Insert the pipe andadjust its position M4P2 Prepare the

welding gun M3P2

3 Hold the pipe H Start spot welding M1P0 times 119899

Figure 5 Shooting operatorrsquos operation

6 Case Study

In our study a manual welding case is used to illustrate theproposed OEK acquisitionmethodThis case originates froma real manufacturing companyrsquos production improvementproject

61 Identification of OEK Sources SQ company is a producerof new energy devices The main product of company isLNG (Liquefied Natural Gas) cylinders The welding processis widely used in the cylindersrsquo manufacturing The head ofinner vessel due to its complex internal structure cannot bemanufactured by autowelding Therefore manual welding isemployed in this process (Figure 5)

In actual manufacturing the varying quality of theproduct causes much trouble for the company The majorreason for the productrsquos quality variation lies in the differentwelding methods used by the workers Because the trainingof new employees needs a long time there are always novicesand skilled operators in the company Although the companyprovides the standard operation instructions the novicesusually find it hard to grasp the technique know-how Forthe skilled operators they often feel difficult to tell what theirempirical knowledge is and what kind of operation is the bestin guiding the novice Therefore it is an urgent mission toanalyze the operational know-how of the skilled operatorsand elicit their OEK

62 Motion Modeling

Step 1 (motion capture) Fifteen skilled technicians andfifteen novices were invited to take part in the case In this

step motion capture was carried out in the real operationsituation as shown in Figure 5 It is important for the videocamera to take a position that does not disturb the workerrsquosoperation In our study the wearable sensors were not chosensince they often burden theworker and affect human thinking[24]

Step 2 (motion analysis) TheMODAPTS analysis can trans-late the welding operation into basic motion element classcodes and time values through video examination [9] Forexample in an assembly process the operatorrsquos motionsequence includes ldquomove the left hand to the componentrdquoldquoget the componentrdquo ldquomove the componentrdquo and ldquoput thecomponentrdquo which are assigned the codes M3 G1 M3 andP1 respectively Thus the resulting motion MODAPTS isM3G1M3P1

In our study the welding process is represented alphanu-merically as motion element sets by means of IEMS softwareFigure 6 illustrates an example of a MODAPTS for sequen-tial welding operations consisting of grasping insertingadjusting and welding operations Table 2 presents theMOD representation of a fragment of the operatorrsquos weldingprocess

Step 3 (establish scenario model) In this step the result ofMODAPTS analysis is used to construct the scenario modelThe operatorrsquos motion combination andmotion sequence arethemain part of the scenariomodelThe goal and subgoals ofthe operator are the assistant part of scenario model

The scenario factors are considered in the scenariomodelIn our case the ldquoman-machine interactionrdquo includes twoparts the interaction between operator and objective and theinteraction between operator and tools

In the part of man-object interaction the factors consistof the position of the welding arc starting point the numberof rotations welding a pipe and the welding mode

In the part ofman-tool interaction the factors include thewelding gun posture in the difficult process part

The scenario model is shown in Figure 7

Step 4 (establish integrated model) The integrated modelis established by attaching the quality information of eachoperation to the scenario model

In the case the quality of welding process is evaluated bythe standard of the enterpriseThe evaluation criteria includethe weld surface color and the quality of the weld seam Theweld colors include white yellow and blackWhite is the best


(a) A case of welding process

② M3④ M1④ P2

③ P2

① M4

③ M4② G1

⑤ H

② M3

⑤ P0 ④ M1

⑤ P0 ④ M1

⑤ P0 ④ M1

⑤ P0

(b) A MODAPTS analysis of welding process

Figure 6 The MODAPTS analysis of a welding process fragment

and black is the worst In addition the weld surface quality isdivided into three classes good medium and poor

The integrated model is shown in Figure 8

63 OEK Content Eliciting

(1) PreprocessingThe preprocessing is the fundamental of theeliciting algorithm in the next stage

According to the characteristics of motion sequence andthe requirement of VPRS algorithm the following steps areconducted

Step 1 Remove themeaninglessmotionswhich have nothingto do with the operation such as drinking and talking

Step 2 Combine the repetitive motions for example com-bining M2P1M2P1M2P1 into M2P1 times 3 If the repetitive timeofmotion combination ismore than 3 the time is representedas 119899

The structured result of preprocessing is shown in Table 3The explanation of header of Table 3 is as follows ID indicateseach operator Steps indicate the subgoals in the operationalprocess Arcing point the number of rotations and modechoosing are scenario factors Color and surface quality arethe evaluation indices of welding process quality In thecontent of table null indicates no operation in the step

(2) Eliciting Process In this step the initial OEK content iselicited by means of VPRS based on the result of preprocess-ing and is shown in Table 4

64 Intent Analysis In this case the skilled technicians whoparticipated in the experiment were interviewed to explainthe specific intent behind the motion The evaluation factorsnecessity and intent of the motions are listed in Table 5

65 Postprocessing In this stage the related factors of prob-lem scenarios contributors and creditability are integratedinto the OEK content A completed representation of theskilled operatorsrsquo OEK is obtained from the above stagesThestructured representation of OEK is shown in Table 6

7 Discussion

71 Comparison of OEK between Skilled Technicians andNovices The OEK obtained by our method is the specificknow-how coming from the experience of skilled operatorsuch knowledge indicates themotional difference in the criti-cal operational steps between skilled and unskilled operatorsIn this section wewill discuss such difference between skilledoperatorsrsquo OEK and novicesrsquo motions in order to evaluate thevalue of OEK

We analyze the difference from two aspects of timeconsumption and energy consumption performance in thesame operating procedures (Step 3 Step 5 and Step 6)We calculate the operatorsrsquo time from the operation videosIn order to measure the energy consumption of differentoperators CATIA as leading human factors engineeringsoftware is used as the platform to model the human motionand provide the energy consumption Figure 9 is the CATIAsimulation of different operators The results are shown inFigure 10

The difference of time consumption (156 s and 105 s)between skilled and unskilled operators is obvious accordingto Figure 10(a) However to our surprise the energy con-sumption of unskilled operators is more than three times thatof skilled operators (1190 cal and 313 cal see Figure 10(b))The plausible explanation is the importance of OEK whichnot only improves the product quality but also releases theoperatorsrsquo fatigue and then increases the operatorsrsquo safetyFurthermore the result of comparison demonstrates theaccuracy of OEK acquired by our method

72 Comparison of Our Method with Related Works Theevaluation framework of requirements of elicitation tech-nique is used in our study It is regarded as a reasonable andacceptablemethod to evaluate elicitation technique althoughit is a qualitative evaluation method [49] The evaluationframework includes four factors (elicitor informant problemdomain and elicitation process) and each factor includessome detailed attributes In the evaluation framework acompiled technique adequacy table is used to select the mostadequate techniques for a specific application context [50]In this section the evaluation framework is revised and usedto compare our study with related works in the context of


M4

Position

Inspecting

Put

Start

Agent A

Grasp

Agent B SystemMotions line Agent N

M3

Processed objects

M3

No 1 pipe

M3

ldquoGoa

lrdquoPi

pes w

eldin

g

Num

ber 1

pip

e weld

ing

Weld

ing

prep

arat

ion

Weld

ing

Posit

ion

Weld

ing

mat

eria

ls pr

epar

atio

n

Subg

oal 2

Su

bgoa

l 1

Subg

oal 1

P2

M1

G0

G1

E2

P5

M3

P1

P1

M3

M1

G1

P2

M1

P1

P1

ldquoSub

goalrdquo

Qua

lity

insp

ectio

n

ldquoSub

goalrdquo

Fiel

d in

spec

tion

Subg

oal 1

Welding

M1

G0

D3

G1

M3

D3

M3

M3

D3

M3

E2

ldquoSce

nario

fact

orsrdquo

M

an-m

achi

ne in

tera

ctio

n

Hum

an-a

rtifa

cts

inte

ract

ion

ldquoSub

goalrdquo

Hum

an-to

ols

inte

ract

ion

The n

umbe

r of

rota

tions

Arc

ing

poin

t

ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo

Ges

ture

of h

oldi

ng

tool

Subg

oal 2

Su

bgoa

l 1

Subg

oal 1

Mod

e cho

osin

g

Subg

oal 3

D3 D3 D3

6 12 12

Combination Combination Succession

ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo

14 circle14 circle34 circle

middot middot middotmiddot middot middotmiddot middot middot

Figure 7 The scenario model of the case


M4

Position

Inspecting

Put

Start

Agent A

Grasp


M3

ProcessedobjectsM3

Number 1

M3

ldquoGoa

lrdquoPi

pes w

eldin

g

ldquoSub

goalrdquo

Num

ber 1

pip

e weld

ing

ldquoSub

goalrdquo

Weld

ing

prep

arat

ion

ldquoSub

goalrdquo

Weld

ing

ldquoSub

goalrdquo

Posit

ion

ldquoSub

goalrdquo

Weld

ing

mat

eria

lspr

epar

atio

n

Subg

oal 2

Subg

oal 1

Subg

oal 1

P2

M1G0

G1

E2

P5

M3

P1

P1

M3

M1

G1

P2

M1

P1

P1

ldquoSub

goalrdquo

Qua

lity

insp

ectio

n

ldquoSub

goalrdquo

Fiel

d in

spec

tion

Subg

oal 1

WeldingM1G0

D3

G1

M3

D3

M3

M3

D3

M3

E2

ldquoSce

nario

fact

orsrdquo

Man

-mac

hine

inte

ract

ion

Hum

an-a

rtifa

cts

inte

ract

ion

Hum

an-to

ols

inte

ract

ion

The n

umbe

r of

rota

tions

Arc

ing

poin

tG

estu

re o

fho

ldin

g to

ol

Subg

oal 2

Subg

oal 1

Subg

oal 1

Mod

e cho

osin

g

Subg

oal 3

ldquoQua

lity

fact

orsrdquo

The q

ualit

y of

weld

ing

The s

urfa

ce q

ualit

yof

weld

ing

The c

olor

of

weld

ing

D3 D3 D3

6 12 12


Black Black Black

Non-well-distributed

Well-distributed

Well-distributed

ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo


middot middot middotmiddot middot middot middot middot middot

pipe

Figure 8 The integrated model of the case


Table3Th

eresulto

fpreprocessin

g

IDStep1

Step2

Step3

Step4

Step5

Step6

Step7

Righth

and

Arcingpo

int

Then

umber

ofrotatio

nsMod

echoo

sing

Color

Surfa

cequ

ality

1M4P

2M3P

1times119899-M

3

M3-M1P0-

M4-M2P

1-M4

M3P

1M4P

2M4P

2M3P

1M2P

1times119899

M3P

1times119899-M

3M1P0times119899

14circle

8Com

binatio

nYellow

Well-

distr

ibuted

2M3P

2M1G

0times119899-M

3M4-M2P

1times119899-M

4Null

Null

M1P1times

119899M1G

0times119899-M

3M3P

1times119899

24circle

12Successio

nBlack

Well-

distr

ibuted

3M3P

2M1P1

times119899-M

3M4-M2P

1times119899-M

4Null

Null

M1P1times

119899M1G

0times119899-M

3M1P0times119899

24circle

12Successio

nBlack

Well-

distr

ibuted

4M3P

2M3P

1times119899-M

3M4-M2P

1times119899-M

4Null

Null

M2P

1times119899

M3P

1times119899-M

3M1P0times119899

14circle

12Com

binatio

nYello

wWell-

distr

ibuted

5M3P

2M1P1

times119899-M

3M4-M2P

1times119899-M

4Null

Null

M1P1times

119899M1G

0times119899-M

3M1P0times119899

14circle

12Successio

nBlack

Well-

distr

ibuted

6M3P

2M4P

1times119899-M

3M4-M2P

1times119899-M

4M3P

1M4P

2M4P

2M3P

1M2P

1times119899

M1G

0times119899-M

3M1P0times119899

14circle

8Com

binatio

nYellow

Non

-well-

distr

ibuted

7M3P

2M2P

1times119899-M

3M4-M2P

1times119899-M

4Null

Null

M1P1times

119899M1G

0times119899-M

3M1P0times119899

14circle

12Successio

nYello

wWell-

distr

ibuted

8M3P

5M3P

1times119899-M

3M4-M2P

1times119899-M

4M3P

1M4P

2M4P

2M3P

1M2P

1times119899

M4-M3P

1-M3

M3P

1times119899

14circle

12Com

binatio

nYello

wWell-

distr

ibuted

9M3P

2M2P

1times119899-M

3M3-M1P0-

M3

Null

Null

M1P1times

119899M1G

0times119899-M

3M3P

1times119899

14circle

12Successio

nBlack

Well-

distr

ibuted

10M2P

0M1G

0times119899-M

3M3-M1P0-

M3

Null

Null

M2P

1times119899

M4-M3P

1-M3

M3P

1times119899

24circle

11Successio

nBlack

Well-

distr

ibuted

11M2P

0M1G

0times119899-M

3M3-M1P0-

M3

Null

Null

M2P

1times119899

M1G

0times119899-M

3M3P

1times119899

24circle

12Successio

nBlack

Well-

distr

ibuted

12M3P

2M1G

0times119899-M

3

M3-M1P0-

M4-M2P

1-M4

Null

Null

M1P1times

119899M1G

0times119899-M

3M1P0times119899

24circle

6Com

binatio

nBlack

Non

-well-

distr

ibuted

13M2P

0M1G

0times119899-M

3M3-M1P0-

M3

Null

Null

M2P

1times119899

M1G

0times119899-M

3M3P

1times119899

24circle

12Successio

nBlack

Well-

distr

ibuted

14M4P

2M4P

1times119899-M

3M3-M1P0-

M3

M3P

1M4P

2M4P

2M3P

1M2P

1-M1P1

times119899

M2P

1times119899-M

3M3P

1times119899

24circle

16Com

binatio

nYello

wNon

-well-

distr

ibuted

15M3P

2M1G

0times119899-M

3M4-M2P

1times119899-M

4Null

Null

M2P

1times119899

M1G

0times119899-M

3M3P

1times119899

24circle

12Successio

nBlack

Well-

distr

ibuted

16M3P

5M3

times119899-M

3M4-M2P

1times119899-M

4M3P

1M4P

2M4P

2M3P

1M1P1times

119899M3times119899-M

3M1P0times119899

24circle

6Com

binatio

nBlack

Non

-well-

distr

ibuted

17M4M

3P2

M3

times119899-M

3M4-M2P

1times119899-M

4Null

Null

M1P1times

119899M3times119899-M

3M1P0times119899

34circle

6Com

binatio

nBlack

Non

-well-

distr

ibuted

18M3P

5M1G

0times119899-M

3M4-M2P

1times119899-M

4Null

Null

M2P

1-M1P1

times119899

M3times119899-M

3M1P0times119899

34circle

6Com

binatio

nBlack

Non

-well-

distr

ibuted


(a) Skilled technicianrsquos operation (b) Novicersquos operation

Figure 9 Operational simulation in CATIA

156

105

0

2

4

6

8

10

12

14

16

18

Unskillful operators Skillful operators

Time consumption (s)


(a) Comparison of time consumption

1190

313

0

200

400

600

800

1000

1200

1400


Energy consumption (cal)


(b) Comparison of energy consumption

Figure 10 Comparison of time consumption and energy consumption

engineering empirical knowledge elicitation The advantageand adequacy level of our method under the context ofengineering-oriented OEK acquisition are discussed as fol-lows

The classical qualitative methods (observation [8]) andapprenticeship [7]) a kind of quantitate method [14] anda kind of mixed method [5] are selected to be com-pared with our work Three experts of knowledge manage-ment (two experts are university professors of China andone expert is enterprisersquos senior manager) were invited toevaluate these methods using evaluation framework Theexhaustive description of factors is in the address httpwwwgriseupmessitesextras4adequacypdf The compar-ison among these methods is shown in Table 7

In Table 7 the attribute values are expressed as thenumber of high medium and low values Specifically inthe factors of elicitor and informant the method with fewerrequirements of person (elicitor and informant) is betterAccordingly the number of lowmedium andhigh values is 21 and 0 separately However in the factors of problemdomainand elicitation process the method with higher capabilities

in the problem domain and elicitation process is betterAccordingly the number of high medium and low values is2 1 and 0 separately and the number of short medium andlong values in the attribute of process time is 2 1 and 0 Theevaluation result is shown in the column of total score Theperformance of our method is best

The performance of methods in each factor is explainedin detail as follows (1) The first factor is the factor ofelicitor This factor includes two attributes requirement ofelicitation techniques and domain knowledge The mediumelicitation techniques and domain knowledge are requiredin our method for standard procedure is provided in ourmethod However the superior elicitation techniques anddomain knowledge are required in the qualitative methodsand mixed methods which rely on more interviews orobservation and thus require more elicitation techniquesand domain knowledge (2) The second factor is the factorof informant Three attributes are in this factor informantinvolvement in the elicitation process personality of infor-mant and articulability of expertrsquos empirical knowledge Inour method the requirements of informant involvement


Table 4 Initial OEK content

ID Motion factors Scenario factors Product qualityStep 3 Step 5 Step 6 Right hand Arcing point Color Surface quality

001-LK M3-M1P0-M4-M2P1-M4 M4P2M3P1 M2P1 times 119899 M1P0 times 119899 14 circle Yellow Well-distributed002-YKB M4-M2P1 times 119899-M4 M4P2M3P1 M2P1 times 119899 M1P0 times 119899 14 circle Yellow Well-distributed003-CSF M4-M2P1 times 119899-M4 M4P2M3P1 M2P1 times 119899 M3P1 times 119899 14 circle Yellow Well-distributed

Table 5 Intent analysis of critical dimensions

Factors Support Unrelated Oppose Conclusion Effect descriptionStep 3 10 0 0 Useful Obtain the high quality weldsStep 5 10 0 0 Useful Reduce the pause in the welding processStep 6 10 0 0 Useful Easy to operate and control strengthArcing point 8 2 0 Useful Obtain the high quality welds

and communication with informant are less than those inothermethods In addition articulability of expertrsquos empiricalknowledge in our method is medium (3) The third factoris the factor of problem domain Two attributes are in thisfactor problem definedness and knowledge description Ourmethod has obvious advantage compared to other methodsin this factor because the explicit definition and represen-tation of operational empirical knowledge of engineers areintroduced in our study These advantages can be recognizedin the description of OEK acquisition method (Section 5)and case study (Section 6) (4) The fourth factor is thefactor of elicitation process Two attributes are in this factorconvenience of elicitation process and process time Becauseof the explicit and standard knowledge acquisition process inour method our method is more convenient than others inelicitation process The preformation of our method in theprocess time is medium compared with other methods Insummary our method has many advantages compared withother methods under the context of engineering operationalknowledge elicitation

8 Conclusion

81 Contribution In our study we propose a conceptof engineering-oriented operational empirical knowledge(OEK) to describe the engineering-oriented operationalknow-how and provide the definition and representation ofengineering-oriented OEK A novel framework for acquiringengineering-oriented OEK from skilled technicianrsquos opera-tions is presented which can be used to elicit skilled tech-nicianrsquos valuable critical motion and intent The frameworkconstructs a completed knowledge acquisition process fromskilled worksrsquo operation to the structured OEK

The theoretical contribution of this study is multifoldFirst few researches articulate the engineering-orientedOEKand present quantitative acquisition method for this kind ofknowledge Our study attempts to propose operational defi-nition of engineering-oriented OEK and structured acquireframework which is innovation in the field of knowledgeacquisition Our acquisition framework covers the completedtacit knowledge transfer process while traditional methodstend to focus on the particular aspect or phase of tacit

knowledge acquisition Second our research provides thefundamentals for tacit knowledge sharing and reusing

The research findings provide some important applica-tion for practitioners Our method uses ldquothe language ofworkrdquo to describe the technicianrsquos operational process andprovide the exact representation of technicianrsquos operationalknow-how In addition the convenience and standardizationof our method facilitate application in the manufacturingenterprise The OEK obtained from skilled technician can bereplicated and reused within novices

82 Future Work and Limitation There are some limitationsin this study which provide directions for future study

One of the limitations is that knowledge engineers andtechnical professionals are involved in the knowledge acqui-sition process frequently Toomuch human involvement mayreduce the reliability of the acquisition method Howeversome researchers argued that human involvement is indis-pensable for tacit knowledge acquisition In the future how tocontrol the degree of human involvement in OEK acquisitionwill be studied Moreover the subtleties of human motioncannot be thoroughly described by the MODAPTS In thefuture how to identify the subtleties of human motion willbe studied

Though more and more manual operations have beenreplaced by robots in some special manufacturing processesthe manual working is still irreplaceable Therefore ourstudy is valuable for manufacturing enterprises to acquire thecritical operational know-how and improve the training ofnew employees

Competing Interests

The authors declare that they have no competing interests

Acknowledgments

The authors are most grateful to National Natural ScienceFoundation of China (nos 70971085 and 71271133) theResearch Fund for the Doctoral Program of Higher Educa-tion of China (no 20100073110035) and Innovation Program


Table6Structured

representatio

nof

thec

ompleted

OEK

Prob

lem

scenario

Con

tributors

Con

tent

Intent

Creditability

Prob

lem

IDProb

lem

name

Scenario

IDScenario

characteris

ticScenario

attribute

Experts

IDCr

itical

dimensio

n

Motion

combinatio

ndescrip

tion

Intent

descrip

tion

001

Theq

ualityof

weld

ingisno

tconstant

001

Theh

eadof

innerv

essel

Bend

ing

weld

ingargon

arcw

elding

001-L

KStep3

M3-M1P0-M4

-M2P

1-M4

Obtaintheh

igh

quality

weld

s100

002-YK

B003-CS

FStep3

M4-M2P

1times119899-M

4100

001-L

K002-YK

B003-CS

FStep5

M4P

2M3P

1Re

duce

the

pauseinthe

weld

ingprocess

100

001-L

K002-YK

B003-CS

FStep6

M2P

1times119899

Easy

toop

erate

andcontrol

strength

100

001-L

K002-YK

B003-CS

FArcingpo

int

14circle

Obtaintheh

igh

quality

weld

s80


Table 7 The comparison between our study and other methods

Evaluation index

Values

Methods

Factor Attribute

Bandura [8]andCollis

and Winnips[7]

Nakagawa etal [14] andHuang et al

[15]

Yoshida et al[5] This study

Elicitor

Requirementsof elicitationtechniques

Lowmediumhigh M M H M

Knowledge of(familiaritywith) domain

Lowmediumhigh H M H M

Informant

Involvementof informant Lowmediumhigh H M H M

Personality ofinformant Lowmediumhigh H M M L

Articulabilityof knowledge Lowmediumhigh L M M M

Problemdomain

Knowledgedescription Highmediumlow L M L H

Problemdefinedness Highmediumlow L H M H

Elicitationprocess

Convenience Highmediumlow L M M HProcess time Shortmediumlong L L S M

Total score 3 9 6 13

of Shanghai Municipal Education Commission (13ZZ012) forfinancial supports that made this research possible

References

[1] O Brdiczka and V Bellotti ldquoIdentifying routine and telltaleactivity patterns in knowledge workrdquo in Proceedings of the FifthIEEE International Conference on Semantic Computing (ICSCrsquo11) pp 95ndash101 IEEE Palo Alto Calif USA September 2011

[2] L T Nguyen and J Zhang ldquoUnsupervised work knowledgemining through mobility and physical activity sensingrdquo inProceedings of the 6th International Conference on MobileComputing Applications and Services (MobiCASE rsquo14) pp 30ndash39 IEEE November 2014

[3] A Chennamaneni and J T C Teng ldquoAn integrated frameworkfor effective tacit knowledge transferrdquo in Proceedings of the 17thAmericas Conference on Information Systems 2011 (AMCIS rsquo11)pp 2471ndash2480 Detroit Mich USA August 2011

[4] AHaug ldquoThe illusion of tacit knowledge as the great problem inthe development of product configuratorsrdquoArtificial Intelligencefor Engineering Design Analysis and Manufacturing vol 26 no1 pp 25ndash37 2012

[5] I Yoshida Y Teramoto H Tabata C Han and H HashimotoldquoTacit knowledge extraction of skillful operation from expertengineersrdquo in Proceedings of the IEEEASME InternationalConference on Advanced Intelligent Mechatronics (AIM rsquo11) pp554ndash559 IEEE Budapest Hungary July 2011

[6] R K Wagner and R J Sternberg ldquoPractical intelligence inreal-world pursuits the role of tacit knowledgerdquo Journal ofPersonality and Social Psychology vol 49 no 2 pp 436ndash4581985

[7] B Collis and K Winnips ldquoTwo scenarios for productivelearning environments in the workplacerdquo British Journal ofEducational Technology vol 33 no 2 pp 133ndash148 2002

[8] A Bandura Social Learning Theory General Learning PressNew York NY USA 1977

[9] H Cho S Lee and J Park ldquoTime estimation method formanual assembly using MODAPTS technique in the productdesign stagerdquo International Journal of Production Research vol52 no 12 pp 3595ndash3613 2014

[10] I Nonaka ldquoA dynamic theory of organizational knowledgecreationrdquo Organization Science vol 5 no 1 pp 14ndash37 1994

[11] M G Sobol and D Lei ldquoEnvironment manufacturing technol-ogy and embedded knowledgerdquo International Journal ofHumanFactors in Manufacturing vol 4 no 2 pp 167ndash189 1994

[12] H Hashimoto I Yoshida Y Teramoto H Tabata and CHan ldquoExtraction of tacit knowledge as expert engineerrsquos skillbased on mixed human sensingrdquo in Proceedings of the 20thIEEE International Symposium on Robot and Human InteractiveCommunication (RO-MAN rsquo11) pp 413ndash418 IEEE Atlanta GaUSA August 2011

[13] K Watanuki ldquoA mixed reality-based emotional interactionsand communications for manufacturing skills trainingrdquo inEmotional Engineering S Fukuda Ed pp 39ndash61 SpringerLondon UK 2011

[14] J Nakagawa Q An Y Ishikawa et al ldquoExtraction and evalua-tion of proficiency in bed care motion for education service ofnursing skillrdquo in Proceedings of the 2nd International Conferenceon Serviceology (ICServ rsquo14) pp 91ndash96 2014

[15] ZHuang ANagataM Kanai-Pak et al ldquoAutomatic evaluationof trainee nursesrsquo patient transfer skills using multiple kinectsensorsrdquo IEICE Transactions on Information and Systems vol97 no 1 pp 107ndash118 2014


[16] N Li A J Schreiber W Cohen and K Koedinger ldquoEfficientcomplex skill acquisition through representation learningrdquoAdvances in Cognitive Systems no 2 pp 149ndash166 2012

[17] K Mitsuhashi H Hashimoto and Y Ohyama ldquoMotion curvedsurface analysis and composite for skill succession using RGBDcamerardquo in Proceedings of the 12th International Conference onInformatics in Control Automation and Robotics (ICINCO rsquo15)pp 406ndash413 Alsace France July 2015

[18] I Nonaka and H TakeuchiThe Knowledge-Creating CompanyHow Japanese Companies Create the Dynamics of InnovationOxford University Press New York NY USA 1995

[19] I Nonaka and R Toyama ldquoThe knowledge-creating theoryrevisited knowledge creation as a synthesizing processrdquoKnowl-edge Management Research amp Practice vol 1 no 1 pp 2ndash102003

[20] K M Ford J M Bradshaw J R Adams-Webber and N MAgnew ldquoKnowledge acquisition as a constructive modelingactivityrdquo International Journal of Intelligent Systems vol 8 no1 pp 9ndash32 1993

[21] W Y Zhang S B Tor and G A Britton ldquoA two-levelmodelling approach to acquire functional design knowledgein mechanical engineering systemsrdquo International Journal ofAdvancedManufacturing Technology vol 19 no 6 pp 454ndash4602002

[22] J N Liu YHu andYHe ldquoA set covering based approach to findthe reduct of variable precision rough setrdquo Information SciencesAn International Journal vol 275 pp 83ndash100 2014

[23] L Liu Z Jiang and B Song ldquoA novel two-stage methodfor acquiring engineering-oriented empirical tacit knowledgerdquoInternational Journal of Production Research vol 52 no 20 pp5997ndash6018 2014

[24] L T Nguyen and J Zhang ldquoUnsupervised work knowledgemining through mobility and physical activity sensingrdquo inProceedings of the 6th International Conference on MobileComputing Applications and Services (MobiCASE rsquo14) pp 30ndash39 IEEE Austin Tex USA November 2014

[25] J Liu andXHu ldquoA reuse oriented representationmodel for cap-turing and formalizing the evolving design rationalerdquo ArtificialIntelligence for Engineering Design Analysis andManufacturingvol 27 no 4 pp 401ndash413 2013

[26] S Popadiuk and C W Choo ldquoInnovation and knowledgecreation how are these concepts relatedrdquo International Journalof Information Management vol 26 no 4 pp 302ndash312 2006

[27] S S R Abidi Y-NCheah and J Curran ldquoA knowledge creationinfo-structure to acquire and crystallize the tacit knowledge ofhealth-care expertsrdquo IEEE Transactions on Information Technol-ogy in Biomedicine vol 9 no 2 pp 193ndash204 2005

[28] L Zhongtu W Qifu and C Liping ldquoA knowledge-basedapproach for the task implementation in mechanical productdesignrdquo The International Journal of Advanced ManufacturingTechnology vol 29 no 9 pp 837ndash845 2006

[29] D Leonard-Barton ldquoCore capabilities and core rigidities aparadox in managing new product developmentrdquo StrategicManagement Journal vol 13 no S1 pp 111ndash125 1992

[30] A Abecker S Aitken F Schmalhofer and B TschaitschianldquoKARATEKIT tools for the knowledge-creating companyrdquo inProceedings of the Knowledge Acquisition for Knowledge-BasedSystems Workshop (KAW rsquo98) pp 18ndash23 Banff Canada April1998

[31] G CHeydeModapts Plus HeydeDynamics Sydney Australia1983

[32] D W Karger and F H Bayha Engineered Work MeasurementThe Industrial Press New York NY USA 1966

[33] K B Zandin MOST Work Measurement Systems CRC PressBoca Raton Fla USA 2002

[34] A Freivalds and J Goldberg ldquoSpecification of base for variablerelaxation allowancerdquo Journal of Methods Time Measurementno 14 pp 2ndash29 1988

[35] H Cho and J Park ldquoMotion-based method for estimatingtime required to attach self-adhesive insulatorsrdquoCADComputerAided Design vol 56 pp 68ndash87 2014

[36] B W Niebel Motion and Time Study Irwin Homewood IllUSA 8th edition 1988

[37] L de Brabandere and A Iny ldquoScenarios and creativity thinkingin new boxesrdquo Technological Forecasting and Social Change vol77 no 9 pp 1506ndash1512 2010

[38] P Brezillon ldquoContext in problem solving a surveyrdquo KnowledgeEngineering Review vol 14 no 1 pp 47ndash80 1999

[39] C Y Potts ldquoUsing schematic scenarios to understand userneedsrdquo in Proceedings of the ACM 1st Conference on DesigningInteractive Systems Processes Practices Methods amp Techniques(DIS rsquo95) pp 247ndash256 1995

[40] Y Leung W-Z Wu andW-X Zhang ldquoKnowledge acquisitionin incomplete information systems a rough set approachrdquoEuropean Journal of Operational Research vol 168 no 1 pp164ndash180 2005

[41] J-S Mi W-Z Wu and W-X Zhang ldquoApproaches to knowl-edge reduction based on variable precision rough set modelrdquoInformation Sciences vol 159 no 3-4 pp 255ndash272 2004

[42] Z Pawlak ldquoRough setsrdquo International Journal of Computer ampInformation Sciences vol 11 no 5 pp 341ndash356 1982

[43] W Ziarko ldquoVariable precision rough set modelrdquo Journal ofComputer and System Sciences vol 46 no 1 pp 39ndash59 1993

[44] C-T Su and J-H Hsu ldquoPrecision parameter in the variableprecision rough sets model an applicationrdquo Omega vol 34 no2 pp 149ndash157 2006

[45] J W Grzymala-Busse ldquoLERSmdasha system for learning fromexamples based on rough setsrdquo in Intelligent Decision SupportHandbook of Applications and Advances of the Rough Sets The-ory R Slowinski Ed pp 3ndash18 Kluwer Academic DordrechtThe Netherlands 1992

[46] X Pan S Zhang H Zhang X Na and X Li ldquoA variableprecision rough set approach to the remote sensing landusecover classificationrdquo Computers amp Geosciences vol 36 no12 pp 1466ndash1473 2010

[47] D M Wegner ldquoPrecis of the illusion of conscious willrdquoBehavioral and Brain Sciences vol 27 no 5 pp 649ndash659 2004

[48] H S Kim J M Kim and W G Lee ldquoIE behavior intent astudy on ICT ethics of college students in koreardquo Asia-PacificEducation Researcher vol 23 no 2 pp 237ndash247 2014

[49] Y Rana and A Tamara ldquoAn enhanced requirements elicita-tion framework based on business process modelsrdquo ScientificResearch and Essays vol 10 no 7 pp 279ndash286 2015

[50] D Carrizo O Dieste and N Juristo ldquoSystematizing require-ments elicitation technique selectionrdquo Information and SoftwareTechnology vol 56 no 6 pp 644ndash669 2014

Submit your manuscripts athttpwwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

MathematicsJournal of


Mathematical Problems in Engineering

Hindawi Publishing Corporationhttpwwwhindawicom

Differential EquationsInternational Journal of

Volume 2014

Applied MathematicsJournal of


Probability and StatisticsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of


Mathematical PhysicsAdvances in

Complex AnalysisJournal of


OptimizationJournal of


CombinatoricsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of


Operations ResearchAdvances in

Journal of


Function Spaces

Abstract and Applied AnalysisHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of Mathematics and Mathematical Sciences


The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014


Algebra

Discrete Dynamics in Nature and Society



Decision SciencesAdvances in

Discrete MathematicsJournal of


Volume 2014 Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Stochastic AnalysisInternational Journal of


qualitative methods The disadvantages of these methodsare obvious they are inefficient time-consuming and notreplicable for different acquisition results will occur due todifferent cognition and learning modes of observers There-fore development of an efficient and quantitative method toelicit operational know-how becomes an emergent task in thefield of knowledge management

Aiming at the obstacles in the field of engineering-oriented operational know-how acquisition we proposea concept of engineering-oriented operational empiricalknowledge (OEK) to describe this kind of knowledge andthen present an explicit definition and the representationof engineering-oriented OEK A novel OEK acquisition(OEKA) framework is provided in our study which cancomplete the transformation from technicianrsquos operationalmotion to the structured OEKThe OEKA integrates motionanalysis motion elicitation and intent analysis First theworkerrsquos specific operation process is divided into basicmotion elements by means of modular arrangement of pre-determined time standards (MODAPTS) which is a systemused to analyze the performance of manual work [9] Secondwe combine the motion elements with scenario factors toestablish the scenario model and then the algorithm ofvariable precision rough set (VPRS) is used to extract thecritical motion of skilled technicians Third the skilledtechnicianrsquos intent behind theOEK content is elicited with theaid of interview At last the structured representation of OEKis constructed in the postprocessing

Our contribution in this work is threefold

(1) We define the concept of engineering-oriented OEKand provide the structured representation of OEK

(2) We propose an integrated OEK acquisition frame-work integrating motion analysis motion elicitationand intent analysis by which empirical knowledge inthe engineerrsquos operation can be obtained

(3) We demonstrate that the extracted OEK can capturethe technical know-how and can be used for novicesrsquoskill training

The rest of the paper is organized as follows we firstreview the related works in Section 2 In Section 3 we definethe concept of OEK and then provide the representationmodel of OEK In Section 4 we present the OEK acquisitionmethod and its advantage Section 5 describes the completeprocess of OEK acquisition Section 6 provides a detailedcase study The comparison of our method with some relatedworks is discussed in Section 7 Conclusions and futurestudies are outlined in the last section

2 Related Works

21 Tacit Knowledge and Tacit Knowledge AcquisitionKnowledge tied to the senses physical experiences move-ment skills intuition unarticulated mental models orimplicit rules of thumb is tacit [18 19] Tacit knowledge isrooted in the action procedures routines ideals commit-ment and emotion which makes it differ from the explicitknowledge that can be uttered and obtained as drawing and

writing Individuals with tacit knowledge are often unawareof what they possess and how valuable they are for others

Tacit knowledge acquisition means eliciting tacit knowl-edge from various knowledge sources and representing it inthe right form in order to share the knowledge among orga-nizational members and reuse it [20 21] Up till now manytacit knowledge extractionmethods have been developed andapplied in the fields of software engineering manufacturingengineering and health care [22 23] Many of these methodsare based on literal documents And with the developmentof natural language processing machine learning the tacitknowledge acquisitionmethods based on document aremoreand more efficient and valuable

Although the methods based on literal document aresuitable for acquiring the explanation of procedures theyare difficult to acquire the essence of the operational know-how [24] In addition traditional knowledge extractiontechniques work well in eliciting tacit knowledge in literaldocuments They do not elicit the intent of the documentsHowever intent is an indispensable part of tacit knowledgethe intent elicitation should be integrated in the tacit knowl-edge acquisition method [25]

22 Operational Know-How and Knowledge AcquisitionOperational know-how is regarded as a kind of tacit knowl-edge [5] Such knowledge does not originate from theliterature or documents identified as explicit knowledgebut originates from the experience of an individual [26]Consequently it is difficult to be expressed and transferred[27 28]

Many scholars have committed to obtaining the opera-tional know-how and proposed many acquisition models ormethods In Table 1 the existing literatures about operationalknow-how acquisition are analyzed from the perspectivesof method description qualitative or quantitative object ofextraction applied technology application field and draw-back

These methods are divided into three types qualitativemethod quantitative method and mixed method

Observation and apprenticeship are classic qualitativemethods for extracting the operational know-how of skilledtechnicians [3] Observation is recognized as a valuable formof learning according to the social cognitive theory [8] Theobserver gains insights into the skilled techniciansrsquo practicesand builds his personal knowledge base by observing theactions of the expert [29] Apprenticeship is formal arrange-ments where specialized knowledge or skill from a domainexpert is passed to a novice The novice will acquire thesame level of competence as the expert after extensivelypracticing the skill for a prescribed period of time [7] Thesequalitative acquisition methods are successful at transferringthe operational know-how from experts to novices and arethe focus of earlier literatures However these methods haveobvious drawbacks such as the time-consuming and theinconsistent acquisition results for the different cognition andlearning modes of learners

The quantitativemethod of operational know-how acqui-sition is another type in which the specific algorithm ortool is used to elicit the operational know-how Nakagawa





Object ofextraction



methodActions of




expert


skillNot limited





skilledtechnicians






Qualitativemethod


skilledtechnicians





vague



Quantitativemethod




quantitatively






sensingMixed method



Extracthigh-level


Service










curved surface


motion

Lackingaccurate



















119872119888= 11989811198982sdot sdot sdot 119898119899 (1)


119899is a basic

motion element



119872119904= 11989811988611198981198862sdot sdot sdot 119898119886119895 or 119898


or 11989811988911198981198892sdot sdot sdot 119898119889119897

(2)


119894119899119894 = 119886 119887 119889


combination







Structured OEK


MODAPTS

Start

End


Video analysis

Interview analysis

Motion modeling

Motion eliciting

Intent eliciting


Intent analysis

Postprocessing

OEK sources





4 Methodology




























Action A 1

Action 3

Action n

Action 2

Start

End

Agent A

Action 1


Action A 2

Processed objects

Action B 1


Action A 3

Object 1

Object nAction B 3

ldquoGoa

lrdquoSp

ecifi

c con

tent

of g

oal

Spec

ific c

onte

nt o

f go

alSp

ecifi

c con

tent

of

goal

ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo

Spec

ific c

onte

nt

of g

oal

ldquoSub

goalrdquo

Spec

ific c

onte

nt

of g

oal

ldquoSub

goalrdquo

Spec

ific c

onte

nt o

f go

al

Subg

oal 1

Subg

oal 2

Subg

oal n

Object 2

Action A 3

Action A 3

Action A 3

Action B 1

Action A 3

Action B 3

Action B 3

Action B 3

Action B 3

Action N 1

Action N 3

Action N 1

Action N 3

Action N 3

Action N 3

Action N 3


ldquoSce

nario

fact

orsrdquo

Spec

ific f

acto

rs









cup119886isin119860119881119886 where 119881




119886


1198641 1198642 119864


into 119899 subsets of 1198641 1198642 119864




Action A 1

Action 3

Action n

Action 2

Start

End

Agent A

Action 1


Action A 2

Processed objects

Action B 1

Action A 3

Object 1

Object nAction B 3

ldquoGoa

lrdquoSp

ecifi

c con

tent

of g

oal

ldquoSub

goalrdquo

Spec

ific c

onte

nt o

f go

al

ldquoSub

goalrdquo

Spec

ific c

onte

nt o

f go

al

ldquoSub

goalrdquo

Spec

ific c

onte

nt

of g

oal

ldquoSub

goalrdquo

Spec

ific c

onte

nt

of g

oal

ldquoSub

goalrdquo

Spec

ific c

onte

nt o

f go

al

Subg

oal 1

Su

bgoa

l 2

Subg

oal n

Object 2

Action A 3

Action A 3

Action A 3

Action B 1

Action A 3

Action B 3

Action B 3

Action B 3

Action B 3

Action N 1

Action N 3

Action N 1

Action N 3

Action N 3

Action N 3

Action N 3


ldquoSce

nario

fact

orsrdquo

Spec

ific f

acto

rs


ldquoPro

duct

qua

lityrdquo

Spec

ific f

acto

rs

Parameter A 1

Parameter A 2

Parameter A n

Parameter C 1

Parameter C 2

Parameter C n

Parameter B 1

Parameter B 2

Parameter B n


ActionAgent

Messageaction



119862120573(119863) = ⋃

1minus119875119903(119885|119909119894)le120573

119909119894isin 119864 (119875) (4)


119862120573(119863) = ⋃

1minus119875119903(119885|119909119894)lt1minus120573

119909119894isin 119864 (119875) (5)


119885 sub 119864 (119863)

119875119903(119885 | 119909

119894) =

Card (119885 cap 119909119894)

Card (119883119894)

(6)


120574 (119875119863 120573) =

Card (⋃1minus119875119903(119885|119909119894)

119909119894isin 119864 (119875))

Card (119880) (7)










[119862]minus











119862 larr997888 119862 cup 119886 (9)





End



Then 119862 larr997888 119862 minus 119886 (12)

End For


119877 larr997888 119877 cup 119903 (13)


119866 larr997888 119866 minus ⋃

119903isin119877

[119903] (14)



If 119870 sube ⋃

119878isin(119877minus119903)

[119904] Then 119877 larr997888 119877 minus 119903 (15)

End For













welding gun M3P2



6 Case Study




62 Motion Modeling














② M3④ M1④ P2

③ P2

① M4

③ M4② G1

⑤ H

② M3

⑤ P0 ④ M1

⑤ P0 ④ M1

⑤ P0 ④ M1

⑤ P0














7 Discussion






M4

Position

Inspecting

Put

Start

Agent A

Grasp


M3

Processed objects

M3

No 1 pipe

M3

ldquoGoa

lrdquoPi

pes w

eldin

g

Num

ber 1

pip

e weld

ing

Weld

ing

prep

arat

ion

Weld

ing

Posit

ion

Weld

ing

mat

eria

ls pr

epar

atio

n

Subg

oal 2

Su

bgoa

l 1

Subg

oal 1

P2

M1

G0

G1

E2

P5

M3

P1

P1

M3

M1

G1

P2

M1

P1

P1

ldquoSub

goalrdquo

Qua

lity

insp

ectio

n

ldquoSub

goalrdquo

Fiel

d in

spec

tion

Subg

oal 1

Welding

M1

G0

D3

G1

M3

D3

M3

M3

D3

M3

E2

ldquoSce

nario

fact

orsrdquo

M

an-m

achi

ne in

tera

ctio

n

Hum

an-a

rtifa

cts

inte

ract

ion

ldquoSub

goalrdquo

Hum

an-to

ols

inte

ract

ion

The n

umbe

r of

rota

tions

Arc

ing

poin

t

ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo

Ges

ture

of h

oldi

ng

tool

Subg

oal 2

Su

bgoa

l 1

Subg

oal 1

Mod

e cho

osin

g

Subg

oal 3

D3 D3 D3

6 12 12


ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo





M4

Position

Inspecting

Put

Start

Agent A

Grasp


M3

ProcessedobjectsM3

Number 1

M3

ldquoGoa

lrdquoPi

pes w

eldin

g

ldquoSub

goalrdquo

Num

ber 1

pip

e weld

ing

ldquoSub

goalrdquo

Weld

ing

prep

arat

ion

ldquoSub

goalrdquo

Weld

ing

ldquoSub

goalrdquo

Posit

ion

ldquoSub

goalrdquo

Weld

ing

mat

eria

lspr

epar

atio

n

Subg

oal 2

Subg

oal 1

Subg

oal 1

P2

M1G0

G1

E2

P5

M3

P1

P1

M3

M1

G1

P2

M1

P1

P1

ldquoSub

goalrdquo

Qua

lity

insp

ectio

n

ldquoSub

goalrdquo

Fiel

d in

spec

tion

Subg

oal 1

WeldingM1G0

D3

G1

M3

D3

M3

M3

D3

M3

E2

ldquoSce

nario

fact

orsrdquo

Man

-mac

hine

inte

ract

ion

Hum

an-a

rtifa

cts

inte

ract

ion

Hum

an-to

ols

inte

ract

ion

The n

umbe

r of

rota

tions

Arc

ing

poin

tG

estu

re o

fho

ldin

g to

ol

Subg

oal 2

Subg

oal 1

Subg

oal 1

Mod

e cho

osin

g

Subg

oal 3

ldquoQua

lity

fact

orsrdquo

The q

ualit

y of

weld

ing

The s

urfa

ce q

ualit

yof

weld

ing

The c

olor

of

weld

ing

D3 D3 D3

6 12 12


Black Black Black


Well-distributed

Well-distributed

ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo



pipe



Table3Th

eresulto

fpreprocessin

g

IDStep1

Step2

Step3

Step4

Step5

Step6

Step7

Righth

and

Arcingpo

int

Then

umber

ofrotatio

nsMod

echoo

sing

Color

Surfa

cequ

ality

1M4P

2M3P

1times119899-M

3

M3-M1P0-

M4-M2P

1-M4

M3P

1M4P

2M4P

2M3P

1M2P

1times119899

M3P

1times119899-M

3M1P0times119899

14circle

8Com

binatio

nYellow

Well-

distr

ibuted

2M3P

2M1G

0times119899-M

3M4-M2P

1times119899-M

4Null

Null

M1P1times

119899M1G

0times119899-M

3M3P

1times119899

24circle

12Successio

nBlack

Well-

distr

ibuted

3M3P

2M1P1

times119899-M

3M4-M2P

1times119899-M

4Null

Null

M1P1times

119899M1G

0times119899-M

3M1P0times119899

24circle

12Successio

nBlack

Well-

distr

ibuted

4M3P

2M3P

1times119899-M

3M4-M2P

1times119899-M

4Null

Null

M2P

1times119899

M3P

1times119899-M

3M1P0times119899

14circle

12Com

binatio

nYello

wWell-

distr

ibuted

5M3P

2M1P1

times119899-M

3M4-M2P

1times119899-M

4Null

Null

M1P1times

119899M1G

0times119899-M

3M1P0times119899

14circle

12Successio

nBlack

Well-

distr

ibuted

6M3P

2M4P

1times119899-M

3M4-M2P

1times119899-M

4M3P

1M4P

2M4P

2M3P

1M2P

1times119899

M1G

0times119899-M

3M1P0times119899

14circle

8Com

binatio

nYellow

Non

-well-

distr

ibuted

7M3P

2M2P

1times119899-M

3M4-M2P

1times119899-M

4Null

Null

M1P1times

119899M1G

0times119899-M

3M1P0times119899

14circle

12Successio

nYello

wWell-

distr

ibuted

8M3P

5M3P

1times119899-M

3M4-M2P

1times119899-M

4M3P

1M4P

2M4P

2M3P

1M2P

1times119899

M4-M3P

1-M3

M3P

1times119899

14circle

12Com

binatio

nYello

wWell-

distr

ibuted

9M3P

2M2P

1times119899-M

3M3-M1P0-

M3

Null

Null

M1P1times

119899M1G

0times119899-M

3M3P

1times119899

14circle

12Successio

nBlack

Well-

distr

ibuted

10M2P

0M1G

0times119899-M

3M3-M1P0-

M3

Null

Null

M2P

1times119899

M4-M3P

1-M3

M3P

1times119899

24circle

11Successio

nBlack

Well-

distr

ibuted

11M2P

0M1G

0times119899-M

3M3-M1P0-

M3

Null

Null

M2P

1times119899

M1G

0times119899-M

3M3P

1times119899

24circle

12Successio

nBlack

Well-

distr

ibuted

12M3P

2M1G

0times119899-M

3

M3-M1P0-

M4-M2P

1-M4

Null

Null

M1P1times

119899M1G

0times119899-M

3M1P0times119899

24circle

6Com

binatio

nBlack

Non

-well-

distr

ibuted

13M2P

0M1G

0times119899-M

3M3-M1P0-

M3

Null

Null

M2P

1times119899

M1G

0times119899-M

3M3P

1times119899

24circle

12Successio

nBlack

Well-

distr

ibuted

14M4P

2M4P

1times119899-M

3M3-M1P0-

M3

M3P

1M4P

2M4P

2M3P

1M2P

1-M1P1

times119899

M2P

1times119899-M

3M3P

1times119899

24circle

16Com

binatio

nYello

wNon

-well-

distr

ibuted

15M3P

2M1G

0times119899-M

3M4-M2P

1times119899-M

4Null

Null

M2P

1times119899

M1G

0times119899-M

3M3P

1times119899

24circle

12Successio

nBlack

Well-

distr

ibuted

16M3P

5M3

times119899-M

3M4-M2P

1times119899-M

4M3P

1M4P

2M4P

2M3P

1M1P1times

119899M3times119899-M

3M1P0times119899

24circle

6Com

binatio

nBlack

Non

-well-

distr

ibuted

17M4M

3P2

M3

times119899-M

3M4-M2P

1times119899-M

4Null

Null

M1P1times

119899M3times119899-M

3M1P0times119899

34circle

6Com

binatio

nBlack

Non

-well-

distr

ibuted

18M3P

5M1G

0times119899-M

3M4-M2P

1times119899-M

4Null

Null

M2P

1-M1P1

times119899

M3times119899-M

3M1P0times119899

34circle

6Com

binatio

nBlack

Non

-well-

distr

ibuted




156

105

0

2

4

6

8

10

12

14

16

18





1190

313

0

200

400

600

800

1000

1200

1400


















8 Conclusion








Competing Interests


Acknowledgments



Table6Structured

representatio

nof

thec

ompleted

OEK

Prob

lem

scenario

Con

tributors

Con

tent

Intent

Creditability

Prob

lem

IDProb

lem

name

Scenario

IDScenario

characteris

ticScenario

attribute

Experts

IDCr

itical

dimensio

n

Motion

combinatio

ndescrip

tion

Intent

descrip

tion

001

Theq

ualityof

weld

ingisno

tconstant

001

Theh

eadof

innerv

essel

Bend

ing

weld

ingargon

arcw

elding

001-L

KStep3

M3-M1P0-M4

-M2P

1-M4

Obtaintheh

igh

quality

weld

s100

002-YK

B003-CS

FStep3

M4-M2P

1times119899-M

4100

001-L

K002-YK

B003-CS

FStep5

M4P

2M3P

1Re

duce

the

pauseinthe

weld

ingprocess

100

001-L

K002-YK

B003-CS

FStep6

M2P

1times119899

Easy

toop

erate

andcontrol

strength

100

001-L

K002-YK

B003-CS

FArcingpo

int

14circle

Obtaintheh

igh

quality

weld

s80



Evaluation index

Values

Methods

Factor Attribute


and Winnips[7]


[15]


Elicitor





Informant




Problemdomain



Elicitationprocess




References



























































Volume 2014




Journal of











Journal of


Function Spaces






Algebra













Object ofextraction



methodActions of




expert


skillNot limited





skilledtechnicians






Qualitativemethod


skilledtechnicians





vague



Quantitativemethod




quantitatively






sensingMixed method



Extracthigh-level


Service










curved surface


motion

Lackingaccurate



















119872119888= 11989811198982sdot sdot sdot 119898119899 (1)


119899is a basic

motion element



119872119904= 11989811988611198981198862sdot sdot sdot 119898119886119895 or 119898


or 11989811988911198981198892sdot sdot sdot 119898119889119897

(2)


119894119899119894 = 119886 119887 119889


combination







Structured OEK


MODAPTS

Start

End


Video analysis

Interview analysis

Motion modeling

Motion eliciting

Intent eliciting


Intent analysis

Postprocessing

OEK sources





4 Methodology




























Action A 1

Action 3

Action n

Action 2

Start

End

Agent A

Action 1


Action A 2

Processed objects

Action B 1


Action A 3

Object 1

Object nAction B 3

ldquoGoa

lrdquoSp

ecifi

c con

tent

of g

oal

Spec

ific c

onte

nt o

f go

alSp

ecifi

c con

tent

of

goal

ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo

Spec

ific c

onte

nt

of g

oal

ldquoSub

goalrdquo

Spec

ific c

onte

nt

of g

oal

ldquoSub

goalrdquo

Spec

ific c

onte

nt o

f go

al

Subg

oal 1

Subg

oal 2

Subg

oal n

Object 2

Action A 3

Action A 3

Action A 3

Action B 1

Action A 3

Action B 3

Action B 3

Action B 3

Action B 3

Action N 1

Action N 3

Action N 1

Action N 3

Action N 3

Action N 3

Action N 3


ldquoSce

nario

fact

orsrdquo

Spec

ific f

acto

rs









cup119886isin119860119881119886 where 119881




119886


1198641 1198642 119864


into 119899 subsets of 1198641 1198642 119864




Action A 1

Action 3

Action n

Action 2

Start

End

Agent A

Action 1


Action A 2

Processed objects

Action B 1

Action A 3

Object 1

Object nAction B 3

ldquoGoa

lrdquoSp

ecifi

c con

tent

of g

oal

ldquoSub

goalrdquo

Spec

ific c

onte

nt o

f go

al

ldquoSub

goalrdquo

Spec

ific c

onte

nt o

f go

al

ldquoSub

goalrdquo

Spec

ific c

onte

nt

of g

oal

ldquoSub

goalrdquo

Spec

ific c

onte

nt

of g

oal

ldquoSub

goalrdquo

Spec

ific c

onte

nt o

f go

al

Subg

oal 1

Su

bgoa

l 2

Subg

oal n

Object 2

Action A 3

Action A 3

Action A 3

Action B 1

Action A 3

Action B 3

Action B 3

Action B 3

Action B 3

Action N 1

Action N 3

Action N 1

Action N 3

Action N 3

Action N 3

Action N 3


ldquoSce

nario

fact

orsrdquo

Spec

ific f

acto

rs


ldquoPro

duct

qua

lityrdquo

Spec

ific f

acto

rs

Parameter A 1

Parameter A 2

Parameter A n

Parameter C 1

Parameter C 2

Parameter C n

Parameter B 1

Parameter B 2

Parameter B n


ActionAgent

Messageaction



119862120573(119863) = ⋃

1minus119875119903(119885|119909119894)le120573

119909119894isin 119864 (119875) (4)


119862120573(119863) = ⋃

1minus119875119903(119885|119909119894)lt1minus120573

119909119894isin 119864 (119875) (5)


119885 sub 119864 (119863)

119875119903(119885 | 119909

119894) =

Card (119885 cap 119909119894)

Card (119883119894)

(6)


120574 (119875119863 120573) =

Card (⋃1minus119875119903(119885|119909119894)

119909119894isin 119864 (119875))

Card (119880) (7)










[119862]minus











119862 larr997888 119862 cup 119886 (9)





End



Then 119862 larr997888 119862 minus 119886 (12)

End For


119877 larr997888 119877 cup 119903 (13)


119866 larr997888 119866 minus ⋃

119903isin119877

[119903] (14)



If 119870 sube ⋃

119878isin(119877minus119903)

[119904] Then 119877 larr997888 119877 minus 119903 (15)

End For













welding gun M3P2



6 Case Study




62 Motion Modeling














② M3④ M1④ P2

③ P2

① M4

③ M4② G1

⑤ H

② M3

⑤ P0 ④ M1

⑤ P0 ④ M1

⑤ P0 ④ M1

⑤ P0














7 Discussion






M4

Position

Inspecting

Put

Start

Agent A

Grasp


M3

Processed objects

M3

No 1 pipe

M3

ldquoGoa

lrdquoPi

pes w

eldin

g

Num

ber 1

pip

e weld

ing

Weld

ing

prep

arat

ion

Weld

ing

Posit

ion

Weld

ing

mat

eria

ls pr

epar

atio

n

Subg

oal 2

Su

bgoa

l 1

Subg

oal 1

P2

M1

G0

G1

E2

P5

M3

P1

P1

M3

M1

G1

P2

M1

P1

P1

ldquoSub

goalrdquo

Qua

lity

insp

ectio

n

ldquoSub

goalrdquo

Fiel

d in

spec

tion

Subg

oal 1

Welding

M1

G0

D3

G1

M3

D3

M3

M3

D3

M3

E2

ldquoSce

nario

fact

orsrdquo

M

an-m

achi

ne in

tera

ctio

n

Hum

an-a

rtifa

cts

inte

ract

ion

ldquoSub

goalrdquo

Hum

an-to

ols

inte

ract

ion

The n

umbe

r of

rota

tions

Arc

ing

poin

t

ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo

Ges

ture

of h

oldi

ng

tool

Subg

oal 2

Su

bgoa

l 1

Subg

oal 1

Mod

e cho

osin

g

Subg

oal 3

D3 D3 D3

6 12 12


ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo





M4

Position

Inspecting

Put

Start

Agent A

Grasp


M3

ProcessedobjectsM3

Number 1

M3

ldquoGoa

lrdquoPi

pes w

eldin

g

ldquoSub

goalrdquo

Num

ber 1

pip

e weld

ing

ldquoSub

goalrdquo

Weld

ing

prep

arat

ion

ldquoSub

goalrdquo

Weld

ing

ldquoSub

goalrdquo

Posit

ion

ldquoSub

goalrdquo

Weld

ing

mat

eria

lspr

epar

atio

n

Subg

oal 2

Subg

oal 1

Subg

oal 1

P2

M1G0

G1

E2

P5

M3

P1

P1

M3

M1

G1

P2

M1

P1

P1

ldquoSub

goalrdquo

Qua

lity

insp

ectio

n

ldquoSub

goalrdquo

Fiel

d in

spec

tion

Subg

oal 1

WeldingM1G0

D3

G1

M3

D3

M3

M3

D3

M3

E2

ldquoSce

nario

fact

orsrdquo

Man

-mac

hine

inte

ract

ion

Hum

an-a

rtifa

cts

inte

ract

ion

Hum

an-to

ols

inte

ract

ion

The n

umbe

r of

rota

tions

Arc

ing

poin

tG

estu

re o

fho

ldin

g to

ol

Subg

oal 2

Subg

oal 1

Subg

oal 1

Mod

e cho

osin

g

Subg

oal 3

ldquoQua

lity

fact

orsrdquo

The q

ualit

y of

weld

ing

The s

urfa

ce q

ualit

yof

weld

ing

The c

olor

of

weld

ing

D3 D3 D3

6 12 12


Black Black Black


Well-distributed

Well-distributed

ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo



pipe



Table3Th

eresulto

fpreprocessin

g

IDStep1

Step2

Step3

Step4

Step5

Step6

Step7

Righth

and

Arcingpo

int

Then

umber

ofrotatio

nsMod

echoo

sing

Color

Surfa

cequ

ality

1M4P

2M3P

1times119899-M

3

M3-M1P0-

M4-M2P

1-M4

M3P

1M4P

2M4P

2M3P

1M2P

1times119899

M3P

1times119899-M

3M1P0times119899

14circle

8Com

binatio

nYellow

Well-

distr

ibuted

2M3P

2M1G

0times119899-M

3M4-M2P

1times119899-M

4Null

Null

M1P1times

119899M1G

0times119899-M

3M3P

1times119899

24circle

12Successio

nBlack

Well-

distr

ibuted

3M3P

2M1P1

times119899-M

3M4-M2P

1times119899-M

4Null

Null

M1P1times

119899M1G

0times119899-M

3M1P0times119899

24circle

12Successio

nBlack

Well-

distr

ibuted

4M3P

2M3P

1times119899-M

3M4-M2P

1times119899-M

4Null

Null

M2P

1times119899

M3P

1times119899-M

3M1P0times119899

14circle

12Com

binatio

nYello

wWell-

distr

ibuted

5M3P

2M1P1

times119899-M

3M4-M2P

1times119899-M

4Null

Null

M1P1times

119899M1G

0times119899-M

3M1P0times119899

14circle

12Successio

nBlack

Well-

distr

ibuted

6M3P

2M4P

1times119899-M

3M4-M2P

1times119899-M

4M3P

1M4P

2M4P

2M3P

1M2P

1times119899

M1G

0times119899-M

3M1P0times119899

14circle

8Com

binatio

nYellow

Non

-well-

distr

ibuted

7M3P

2M2P

1times119899-M

3M4-M2P

1times119899-M

4Null

Null

M1P1times

119899M1G

0times119899-M

3M1P0times119899

14circle

12Successio

nYello

wWell-

distr

ibuted

8M3P

5M3P

1times119899-M

3M4-M2P

1times119899-M

4M3P

1M4P

2M4P

2M3P

1M2P

1times119899

M4-M3P

1-M3

M3P

1times119899

14circle

12Com

binatio

nYello

wWell-

distr

ibuted

9M3P

2M2P

1times119899-M

3M3-M1P0-

M3

Null

Null

M1P1times

119899M1G

0times119899-M

3M3P

1times119899

14circle

12Successio

nBlack

Well-

distr

ibuted

10M2P

0M1G

0times119899-M

3M3-M1P0-

M3

Null

Null

M2P

1times119899

M4-M3P

1-M3

M3P

1times119899

24circle

11Successio

nBlack

Well-

distr

ibuted

11M2P

0M1G

0times119899-M

3M3-M1P0-

M3

Null

Null

M2P

1times119899

M1G

0times119899-M

3M3P

1times119899

24circle

12Successio

nBlack

Well-

distr

ibuted

12M3P

2M1G

0times119899-M

3

M3-M1P0-

M4-M2P

1-M4

Null

Null

M1P1times

119899M1G

0times119899-M

3M1P0times119899

24circle

6Com

binatio

nBlack

Non

-well-

distr

ibuted

13M2P

0M1G

0times119899-M

3M3-M1P0-

M3

Null

Null

M2P

1times119899

M1G

0times119899-M

3M3P

1times119899

24circle

12Successio

nBlack

Well-

distr

ibuted

14M4P

2M4P

1times119899-M

3M3-M1P0-

M3

M3P

1M4P

2M4P

2M3P

1M2P

1-M1P1

times119899

M2P

1times119899-M

3M3P

1times119899

24circle

16Com

binatio

nYello

wNon

-well-

distr

ibuted

15M3P

2M1G

0times119899-M

3M4-M2P

1times119899-M

4Null

Null

M2P

1times119899

M1G

0times119899-M

3M3P

1times119899

24circle

12Successio

nBlack

Well-

distr

ibuted

16M3P

5M3

times119899-M

3M4-M2P

1times119899-M

4M3P

1M4P

2M4P

2M3P

1M1P1times

119899M3times119899-M

3M1P0times119899

24circle

6Com

binatio

nBlack

Non

-well-

distr

ibuted

17M4M

3P2

M3

times119899-M

3M4-M2P

1times119899-M

4Null

Null

M1P1times

119899M3times119899-M

3M1P0times119899

34circle

6Com

binatio

nBlack

Non

-well-

distr

ibuted

18M3P

5M1G

0times119899-M

3M4-M2P

1times119899-M

4Null

Null

M2P

1-M1P1

times119899

M3times119899-M

3M1P0times119899

34circle

6Com

binatio

nBlack

Non

-well-

distr

ibuted




156

105

0

2

4

6

8

10

12

14

16

18





1190

313

0

200

400

600

800

1000

1200

1400


















8 Conclusion








Competing Interests


Acknowledgments



Table6Structured

representatio

nof

thec

ompleted

OEK

Prob

lem

scenario

Con

tributors

Con

tent

Intent

Creditability

Prob

lem

IDProb

lem

name

Scenario

IDScenario

characteris

ticScenario

attribute

Experts

IDCr

itical

dimensio

n

Motion

combinatio

ndescrip

tion

Intent

descrip

tion

001

Theq

ualityof

weld

ingisno

tconstant

001

Theh

eadof

innerv

essel

Bend

ing

weld

ingargon

arcw

elding

001-L

KStep3

M3-M1P0-M4

-M2P

1-M4

Obtaintheh

igh

quality

weld

s100

002-YK

B003-CS

FStep3

M4-M2P

1times119899-M

4100

001-L

K002-YK

B003-CS

FStep5

M4P

2M3P

1Re

duce

the

pauseinthe

weld

ingprocess

100

001-L

K002-YK

B003-CS

FStep6

M2P

1times119899

Easy

toop

erate

andcontrol

strength

100

001-L

K002-YK

B003-CS

FArcingpo

int

14circle

Obtaintheh

igh

quality

weld

s80



Evaluation index

Values

Methods

Factor Attribute


and Winnips[7]


[15]


Elicitor





Informant




Problemdomain



Elicitationprocess




References



























































Volume 2014




Journal of











Journal of


Function Spaces






Algebra























119872119888= 11989811198982sdot sdot sdot 119898119899 (1)


119899is a basic

motion element



119872119904= 11989811988611198981198862sdot sdot sdot 119898119886119895 or 119898


or 11989811988911198981198892sdot sdot sdot 119898119889119897

(2)


119894119899119894 = 119886 119887 119889


combination







Structured OEK


MODAPTS

Start

End


Video analysis

Interview analysis

Motion modeling

Motion eliciting

Intent eliciting


Intent analysis

Postprocessing

OEK sources





4 Methodology




























Action A 1

Action 3

Action n

Action 2

Start

End

Agent A

Action 1


Action A 2

Processed objects

Action B 1


Action A 3

Object 1

Object nAction B 3

ldquoGoa

lrdquoSp

ecifi

c con

tent

of g

oal

Spec

ific c

onte

nt o

f go

alSp

ecifi

c con

tent

of

goal

ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo

Spec

ific c

onte

nt

of g

oal

ldquoSub

goalrdquo

Spec

ific c

onte

nt

of g

oal

ldquoSub

goalrdquo

Spec

ific c

onte

nt o

f go

al

Subg

oal 1

Subg

oal 2

Subg

oal n

Object 2

Action A 3

Action A 3

Action A 3

Action B 1

Action A 3

Action B 3

Action B 3

Action B 3

Action B 3

Action N 1

Action N 3

Action N 1

Action N 3

Action N 3

Action N 3

Action N 3


ldquoSce

nario

fact

orsrdquo

Spec

ific f

acto

rs









cup119886isin119860119881119886 where 119881




119886


1198641 1198642 119864


into 119899 subsets of 1198641 1198642 119864




Action A 1

Action 3

Action n

Action 2

Start

End

Agent A

Action 1


Action A 2

Processed objects

Action B 1

Action A 3

Object 1

Object nAction B 3

ldquoGoa

lrdquoSp

ecifi

c con

tent

of g

oal

ldquoSub

goalrdquo

Spec

ific c

onte

nt o

f go

al

ldquoSub

goalrdquo

Spec

ific c

onte

nt o

f go

al

ldquoSub

goalrdquo

Spec

ific c

onte

nt

of g

oal

ldquoSub

goalrdquo

Spec

ific c

onte

nt

of g

oal

ldquoSub

goalrdquo

Spec

ific c

onte

nt o

f go

al

Subg

oal 1

Su

bgoa

l 2

Subg

oal n

Object 2

Action A 3

Action A 3

Action A 3

Action B 1

Action A 3

Action B 3

Action B 3

Action B 3

Action B 3

Action N 1

Action N 3

Action N 1

Action N 3

Action N 3

Action N 3

Action N 3


ldquoSce

nario

fact

orsrdquo

Spec

ific f

acto

rs


ldquoPro

duct

qua

lityrdquo

Spec

ific f

acto

rs

Parameter A 1

Parameter A 2

Parameter A n

Parameter C 1

Parameter C 2

Parameter C n

Parameter B 1

Parameter B 2

Parameter B n


ActionAgent

Messageaction



119862120573(119863) = ⋃

1minus119875119903(119885|119909119894)le120573

119909119894isin 119864 (119875) (4)


119862120573(119863) = ⋃

1minus119875119903(119885|119909119894)lt1minus120573

119909119894isin 119864 (119875) (5)


119885 sub 119864 (119863)

119875119903(119885 | 119909

119894) =

Card (119885 cap 119909119894)

Card (119883119894)

(6)


120574 (119875119863 120573) =

Card (⋃1minus119875119903(119885|119909119894)

119909119894isin 119864 (119875))

Card (119880) (7)










[119862]minus











119862 larr997888 119862 cup 119886 (9)





End



Then 119862 larr997888 119862 minus 119886 (12)

End For


119877 larr997888 119877 cup 119903 (13)


119866 larr997888 119866 minus ⋃

119903isin119877

[119903] (14)



If 119870 sube ⋃

119878isin(119877minus119903)

[119904] Then 119877 larr997888 119877 minus 119903 (15)

End For













welding gun M3P2



6 Case Study




62 Motion Modeling














② M3④ M1④ P2

③ P2

① M4

③ M4② G1

⑤ H

② M3

⑤ P0 ④ M1

⑤ P0 ④ M1

⑤ P0 ④ M1

⑤ P0














7 Discussion






M4

Position

Inspecting

Put

Start

Agent A

Grasp


M3

Processed objects

M3

No 1 pipe

M3

ldquoGoa

lrdquoPi

pes w

eldin

g

Num

ber 1

pip

e weld

ing

Weld

ing

prep

arat

ion

Weld

ing

Posit

ion

Weld

ing

mat

eria

ls pr

epar

atio

n

Subg

oal 2

Su

bgoa

l 1

Subg

oal 1

P2

M1

G0

G1

E2

P5

M3

P1

P1

M3

M1

G1

P2

M1

P1

P1

ldquoSub

goalrdquo

Qua

lity

insp

ectio

n

ldquoSub

goalrdquo

Fiel

d in

spec

tion

Subg

oal 1

Welding

M1

G0

D3

G1

M3

D3

M3

M3

D3

M3

E2

ldquoSce

nario

fact

orsrdquo

M

an-m

achi

ne in

tera

ctio

n

Hum

an-a

rtifa

cts

inte

ract

ion

ldquoSub

goalrdquo

Hum

an-to

ols

inte

ract

ion

The n

umbe

r of

rota

tions

Arc

ing

poin

t

ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo

Ges

ture

of h

oldi

ng

tool

Subg

oal 2

Su

bgoa

l 1

Subg

oal 1

Mod

e cho

osin

g

Subg

oal 3

D3 D3 D3

6 12 12


ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo





M4

Position

Inspecting

Put

Start

Agent A

Grasp


M3

ProcessedobjectsM3

Number 1

M3

ldquoGoa

lrdquoPi

pes w

eldin

g

ldquoSub

goalrdquo

Num

ber 1

pip

e weld

ing

ldquoSub

goalrdquo

Weld

ing

prep

arat

ion

ldquoSub

goalrdquo

Weld

ing

ldquoSub

goalrdquo

Posit

ion

ldquoSub

goalrdquo

Weld

ing

mat

eria

lspr

epar

atio

n

Subg

oal 2

Subg

oal 1

Subg

oal 1

P2

M1G0

G1

E2

P5

M3

P1

P1

M3

M1

G1

P2

M1

P1

P1

ldquoSub

goalrdquo

Qua

lity

insp

ectio

n

ldquoSub

goalrdquo

Fiel

d in

spec

tion

Subg

oal 1

WeldingM1G0

D3

G1

M3

D3

M3

M3

D3

M3

E2

ldquoSce

nario

fact

orsrdquo

Man

-mac

hine

inte

ract

ion

Hum

an-a

rtifa

cts

inte

ract

ion

Hum

an-to

ols

inte

ract

ion

The n

umbe

r of

rota

tions

Arc

ing

poin

tG

estu

re o

fho

ldin

g to

ol

Subg

oal 2

Subg

oal 1

Subg

oal 1

Mod

e cho

osin

g

Subg

oal 3

ldquoQua

lity

fact

orsrdquo

The q

ualit

y of

weld

ing

The s

urfa

ce q

ualit

yof

weld

ing

The c

olor

of

weld

ing

D3 D3 D3

6 12 12


Black Black Black


Well-distributed

Well-distributed

ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo



pipe



Table3Th

eresulto

fpreprocessin

g

IDStep1

Step2

Step3

Step4

Step5

Step6

Step7

Righth

and

Arcingpo

int

Then

umber

ofrotatio

nsMod

echoo

sing

Color

Surfa

cequ

ality

1M4P

2M3P

1times119899-M

3

M3-M1P0-

M4-M2P

1-M4

M3P

1M4P

2M4P

2M3P

1M2P

1times119899

M3P

1times119899-M

3M1P0times119899

14circle

8Com

binatio

nYellow

Well-

distr

ibuted

2M3P

2M1G

0times119899-M

3M4-M2P

1times119899-M

4Null

Null

M1P1times

119899M1G

0times119899-M

3M3P

1times119899

24circle

12Successio

nBlack

Well-

distr

ibuted

3M3P

2M1P1

times119899-M

3M4-M2P

1times119899-M

4Null

Null

M1P1times

119899M1G

0times119899-M

3M1P0times119899

24circle

12Successio

nBlack

Well-

distr

ibuted

4M3P

2M3P

1times119899-M

3M4-M2P

1times119899-M

4Null

Null

M2P

1times119899

M3P

1times119899-M

3M1P0times119899

14circle

12Com

binatio

nYello

wWell-

distr

ibuted

5M3P

2M1P1

times119899-M

3M4-M2P

1times119899-M

4Null

Null

M1P1times

119899M1G

0times119899-M

3M1P0times119899

14circle

12Successio

nBlack

Well-

distr

ibuted

6M3P

2M4P

1times119899-M

3M4-M2P

1times119899-M

4M3P

1M4P

2M4P

2M3P

1M2P

1times119899

M1G

0times119899-M

3M1P0times119899

14circle

8Com

binatio

nYellow

Non

-well-

distr

ibuted

7M3P

2M2P

1times119899-M

3M4-M2P

1times119899-M

4Null

Null

M1P1times

119899M1G

0times119899-M

3M1P0times119899

14circle

12Successio

nYello

wWell-

distr

ibuted

8M3P

5M3P

1times119899-M

3M4-M2P

1times119899-M

4M3P

1M4P

2M4P

2M3P

1M2P

1times119899

M4-M3P

1-M3

M3P

1times119899

14circle

12Com

binatio

nYello

wWell-

distr

ibuted

9M3P

2M2P

1times119899-M

3M3-M1P0-

M3

Null

Null

M1P1times

119899M1G

0times119899-M

3M3P

1times119899

14circle

12Successio

nBlack

Well-

distr

ibuted

10M2P

0M1G

0times119899-M

3M3-M1P0-

M3

Null

Null

M2P

1times119899

M4-M3P

1-M3

M3P

1times119899

24circle

11Successio

nBlack

Well-

distr

ibuted

11M2P

0M1G

0times119899-M

3M3-M1P0-

M3

Null

Null

M2P

1times119899

M1G

0times119899-M

3M3P

1times119899

24circle

12Successio

nBlack

Well-

distr

ibuted

12M3P

2M1G

0times119899-M

3

M3-M1P0-

M4-M2P

1-M4

Null

Null

M1P1times

119899M1G

0times119899-M

3M1P0times119899

24circle

6Com

binatio

nBlack

Non

-well-

distr

ibuted

13M2P

0M1G

0times119899-M

3M3-M1P0-

M3

Null

Null

M2P

1times119899

M1G

0times119899-M

3M3P

1times119899

24circle

12Successio

nBlack

Well-

distr

ibuted

14M4P

2M4P

1times119899-M

3M3-M1P0-

M3

M3P

1M4P

2M4P

2M3P

1M2P

1-M1P1

times119899

M2P

1times119899-M

3M3P

1times119899

24circle

16Com

binatio

nYello

wNon

-well-

distr

ibuted

15M3P

2M1G

0times119899-M

3M4-M2P

1times119899-M

4Null

Null

M2P

1times119899

M1G

0times119899-M

3M3P

1times119899

24circle

12Successio

nBlack

Well-

distr

ibuted

16M3P

5M3

times119899-M

3M4-M2P

1times119899-M

4M3P

1M4P

2M4P

2M3P

1M1P1times

119899M3times119899-M

3M1P0times119899

24circle

6Com

binatio

nBlack

Non

-well-

distr

ibuted

17M4M

3P2

M3

times119899-M

3M4-M2P

1times119899-M

4Null

Null

M1P1times

119899M3times119899-M

3M1P0times119899

34circle

6Com

binatio

nBlack

Non

-well-

distr

ibuted

18M3P

5M1G

0times119899-M

3M4-M2P

1times119899-M

4Null

Null

M2P

1-M1P1

times119899

M3times119899-M

3M1P0times119899

34circle

6Com

binatio

nBlack

Non

-well-

distr

ibuted




156

105

0

2

4

6

8

10

12

14

16

18





1190

313

0

200

400

600

800

1000

1200

1400


















8 Conclusion








Competing Interests


Acknowledgments



Table6Structured

representatio

nof

thec

ompleted

OEK

Prob

lem

scenario

Con

tributors

Con

tent

Intent

Creditability

Prob

lem

IDProb

lem

name

Scenario

IDScenario

characteris

ticScenario

attribute

Experts

IDCr

itical

dimensio

n

Motion

combinatio

ndescrip

tion

Intent

descrip

tion

001

Theq

ualityof

weld

ingisno

tconstant

001

Theh

eadof

innerv

essel

Bend

ing

weld

ingargon

arcw

elding

001-L

KStep3

M3-M1P0-M4

-M2P

1-M4

Obtaintheh

igh

quality

weld

s100

002-YK

B003-CS

FStep3

M4-M2P

1times119899-M

4100

001-L

K002-YK

B003-CS

FStep5

M4P

2M3P

1Re

duce

the

pauseinthe

weld

ingprocess

100

001-L

K002-YK

B003-CS

FStep6

M2P

1times119899

Easy

toop

erate

andcontrol

strength

100

001-L

K002-YK

B003-CS

FArcingpo

int

14circle

Obtaintheh

igh

quality

weld

s80



Evaluation index

Values

Methods

Factor Attribute


and Winnips[7]


[15]


Elicitor





Informant




Problemdomain



Elicitationprocess




References



























































Volume 2014




Journal of











Journal of


Function Spaces






Algebra














Structured OEK


MODAPTS

Start

End


Video analysis

Interview analysis

Motion modeling

Motion eliciting

Intent eliciting


Intent analysis

Postprocessing

OEK sources





4 Methodology




























Action A 1

Action 3

Action n

Action 2

Start

End

Agent A

Action 1


Action A 2

Processed objects

Action B 1


Action A 3

Object 1

Object nAction B 3

ldquoGoa

lrdquoSp

ecifi

c con

tent

of g

oal

Spec

ific c

onte

nt o

f go

alSp

ecifi

c con

tent

of

goal

ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo

Spec

ific c

onte

nt

of g

oal

ldquoSub

goalrdquo

Spec

ific c

onte

nt

of g

oal

ldquoSub

goalrdquo

Spec

ific c

onte

nt o

f go

al

Subg

oal 1

Subg

oal 2

Subg

oal n

Object 2

Action A 3

Action A 3

Action A 3

Action B 1

Action A 3

Action B 3

Action B 3

Action B 3

Action B 3

Action N 1

Action N 3

Action N 1

Action N 3

Action N 3

Action N 3

Action N 3


ldquoSce

nario

fact

orsrdquo

Spec

ific f

acto

rs









cup119886isin119860119881119886 where 119881




119886


1198641 1198642 119864


into 119899 subsets of 1198641 1198642 119864




Action A 1

Action 3

Action n

Action 2

Start

End

Agent A

Action 1


Action A 2

Processed objects

Action B 1

Action A 3

Object 1

Object nAction B 3

ldquoGoa

lrdquoSp

ecifi

c con

tent

of g

oal

ldquoSub

goalrdquo

Spec

ific c

onte

nt o

f go

al

ldquoSub

goalrdquo

Spec

ific c

onte

nt o

f go

al

ldquoSub

goalrdquo

Spec

ific c

onte

nt

of g

oal

ldquoSub

goalrdquo

Spec

ific c

onte

nt

of g

oal

ldquoSub

goalrdquo

Spec

ific c

onte

nt o

f go

al

Subg

oal 1

Su

bgoa

l 2

Subg

oal n

Object 2

Action A 3

Action A 3

Action A 3

Action B 1

Action A 3

Action B 3

Action B 3

Action B 3

Action B 3

Action N 1

Action N 3

Action N 1

Action N 3

Action N 3

Action N 3

Action N 3


ldquoSce

nario

fact

orsrdquo

Spec

ific f

acto

rs


ldquoPro

duct

qua

lityrdquo

Spec

ific f

acto

rs

Parameter A 1

Parameter A 2

Parameter A n

Parameter C 1

Parameter C 2

Parameter C n

Parameter B 1

Parameter B 2

Parameter B n


ActionAgent

Messageaction



119862120573(119863) = ⋃

1minus119875119903(119885|119909119894)le120573

119909119894isin 119864 (119875) (4)


119862120573(119863) = ⋃

1minus119875119903(119885|119909119894)lt1minus120573

119909119894isin 119864 (119875) (5)


119885 sub 119864 (119863)

119875119903(119885 | 119909

119894) =

Card (119885 cap 119909119894)

Card (119883119894)

(6)


120574 (119875119863 120573) =

Card (⋃1minus119875119903(119885|119909119894)

119909119894isin 119864 (119875))

Card (119880) (7)










[119862]minus











119862 larr997888 119862 cup 119886 (9)





End



Then 119862 larr997888 119862 minus 119886 (12)

End For


119877 larr997888 119877 cup 119903 (13)


119866 larr997888 119866 minus ⋃

119903isin119877

[119903] (14)



If 119870 sube ⋃

119878isin(119877minus119903)

[119904] Then 119877 larr997888 119877 minus 119903 (15)

End For













welding gun M3P2



6 Case Study




62 Motion Modeling














② M3④ M1④ P2

③ P2

① M4

③ M4② G1

⑤ H

② M3

⑤ P0 ④ M1

⑤ P0 ④ M1

⑤ P0 ④ M1

⑤ P0














7 Discussion






M4

Position

Inspecting

Put

Start

Agent A

Grasp


M3

Processed objects

M3

No 1 pipe

M3

ldquoGoa

lrdquoPi

pes w

eldin

g

Num

ber 1

pip

e weld

ing

Weld

ing

prep

arat

ion

Weld

ing

Posit

ion

Weld

ing

mat

eria

ls pr

epar

atio

n

Subg

oal 2

Su

bgoa

l 1

Subg

oal 1

P2

M1

G0

G1

E2

P5

M3

P1

P1

M3

M1

G1

P2

M1

P1

P1

ldquoSub

goalrdquo

Qua

lity

insp

ectio

n

ldquoSub

goalrdquo

Fiel

d in

spec

tion

Subg

oal 1

Welding

M1

G0

D3

G1

M3

D3

M3

M3

D3

M3

E2

ldquoSce

nario

fact

orsrdquo

M

an-m

achi

ne in

tera

ctio

n

Hum

an-a

rtifa

cts

inte

ract

ion

ldquoSub

goalrdquo

Hum

an-to

ols

inte

ract

ion

The n

umbe

r of

rota

tions

Arc

ing

poin

t

ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo

Ges

ture

of h

oldi

ng

tool

Subg

oal 2

Su

bgoa

l 1

Subg

oal 1

Mod

e cho

osin

g

Subg

oal 3

D3 D3 D3

6 12 12


ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo





M4

Position

Inspecting

Put

Start

Agent A

Grasp


M3

ProcessedobjectsM3

Number 1

M3

ldquoGoa

lrdquoPi

pes w

eldin

g

ldquoSub

goalrdquo

Num

ber 1

pip

e weld

ing

ldquoSub

goalrdquo

Weld

ing

prep

arat

ion

ldquoSub

goalrdquo

Weld

ing

ldquoSub

goalrdquo

Posit

ion

ldquoSub

goalrdquo

Weld

ing

mat

eria

lspr

epar

atio

n

Subg

oal 2

Subg

oal 1

Subg

oal 1

P2

M1G0

G1

E2

P5

M3

P1

P1

M3

M1

G1

P2

M1

P1

P1

ldquoSub

goalrdquo

Qua

lity

insp

ectio

n

ldquoSub

goalrdquo

Fiel

d in

spec

tion

Subg

oal 1

WeldingM1G0

D3

G1

M3

D3

M3

M3

D3

M3

E2

ldquoSce

nario

fact

orsrdquo

Man

-mac

hine

inte

ract

ion

Hum

an-a

rtifa

cts

inte

ract

ion

Hum

an-to

ols

inte

ract

ion

The n

umbe

r of

rota

tions

Arc

ing

poin

tG

estu

re o

fho

ldin

g to

ol

Subg

oal 2

Subg

oal 1

Subg

oal 1

Mod

e cho

osin

g

Subg

oal 3

ldquoQua

lity

fact

orsrdquo

The q

ualit

y of

weld

ing

The s

urfa

ce q

ualit

yof

weld

ing

The c

olor

of

weld

ing

D3 D3 D3

6 12 12


Black Black Black


Well-distributed

Well-distributed

ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo



pipe



Table3Th

eresulto

fpreprocessin

g

IDStep1

Step2

Step3

Step4

Step5

Step6

Step7

Righth

and

Arcingpo

int

Then

umber

ofrotatio

nsMod

echoo

sing

Color

Surfa

cequ

ality

1M4P

2M3P

1times119899-M

3

M3-M1P0-

M4-M2P

1-M4

M3P

1M4P

2M4P

2M3P

1M2P

1times119899

M3P

1times119899-M

3M1P0times119899

14circle

8Com

binatio

nYellow

Well-

distr

ibuted

2M3P

2M1G

0times119899-M

3M4-M2P

1times119899-M

4Null

Null

M1P1times

119899M1G

0times119899-M

3M3P

1times119899

24circle

12Successio

nBlack

Well-

distr

ibuted

3M3P

2M1P1

times119899-M

3M4-M2P

1times119899-M

4Null

Null

M1P1times

119899M1G

0times119899-M

3M1P0times119899

24circle

12Successio

nBlack

Well-

distr

ibuted

4M3P

2M3P

1times119899-M

3M4-M2P

1times119899-M

4Null

Null

M2P

1times119899

M3P

1times119899-M

3M1P0times119899

14circle

12Com

binatio

nYello

wWell-

distr

ibuted

5M3P

2M1P1

times119899-M

3M4-M2P

1times119899-M

4Null

Null

M1P1times

119899M1G

0times119899-M

3M1P0times119899

14circle

12Successio

nBlack

Well-

distr

ibuted

6M3P

2M4P

1times119899-M

3M4-M2P

1times119899-M

4M3P

1M4P

2M4P

2M3P

1M2P

1times119899

M1G

0times119899-M

3M1P0times119899

14circle

8Com

binatio

nYellow

Non

-well-

distr

ibuted

7M3P

2M2P

1times119899-M

3M4-M2P

1times119899-M

4Null

Null

M1P1times

119899M1G

0times119899-M

3M1P0times119899

14circle

12Successio

nYello

wWell-

distr

ibuted

8M3P

5M3P

1times119899-M

3M4-M2P

1times119899-M

4M3P

1M4P

2M4P

2M3P

1M2P

1times119899

M4-M3P

1-M3

M3P

1times119899

14circle

12Com

binatio

nYello

wWell-

distr

ibuted

9M3P

2M2P

1times119899-M

3M3-M1P0-

M3

Null

Null

M1P1times

119899M1G

0times119899-M

3M3P

1times119899

14circle

12Successio

nBlack

Well-

distr

ibuted

10M2P

0M1G

0times119899-M

3M3-M1P0-

M3

Null

Null

M2P

1times119899

M4-M3P

1-M3

M3P

1times119899

24circle

11Successio

nBlack

Well-

distr

ibuted

11M2P

0M1G

0times119899-M

3M3-M1P0-

M3

Null

Null

M2P

1times119899

M1G

0times119899-M

3M3P

1times119899

24circle

12Successio

nBlack

Well-

distr

ibuted

12M3P

2M1G

0times119899-M

3

M3-M1P0-

M4-M2P

1-M4

Null

Null

M1P1times

119899M1G

0times119899-M

3M1P0times119899

24circle

6Com

binatio

nBlack

Non

-well-

distr

ibuted

13M2P

0M1G

0times119899-M

3M3-M1P0-

M3

Null

Null

M2P

1times119899

M1G

0times119899-M

3M3P

1times119899

24circle

12Successio

nBlack

Well-

distr

ibuted

14M4P

2M4P

1times119899-M

3M3-M1P0-

M3

M3P

1M4P

2M4P

2M3P

1M2P

1-M1P1

times119899

M2P

1times119899-M

3M3P

1times119899

24circle

16Com

binatio

nYello

wNon

-well-

distr

ibuted

15M3P

2M1G

0times119899-M

3M4-M2P

1times119899-M

4Null

Null

M2P

1times119899

M1G

0times119899-M

3M3P

1times119899

24circle

12Successio

nBlack

Well-

distr

ibuted

16M3P

5M3

times119899-M

3M4-M2P

1times119899-M

4M3P

1M4P

2M4P

2M3P

1M1P1times

119899M3times119899-M

3M1P0times119899

24circle

6Com

binatio

nBlack

Non

-well-

distr

ibuted

17M4M

3P2

M3

times119899-M

3M4-M2P

1times119899-M

4Null

Null

M1P1times

119899M3times119899-M

3M1P0times119899

34circle

6Com

binatio

nBlack

Non

-well-

distr

ibuted

18M3P

5M1G

0times119899-M

3M4-M2P

1times119899-M

4Null

Null

M2P

1-M1P1

times119899

M3times119899-M

3M1P0times119899

34circle

6Com

binatio

nBlack

Non

-well-

distr

ibuted




156

105

0

2

4

6

8

10

12

14

16

18





1190

313

0

200

400

600

800

1000

1200

1400


















8 Conclusion








Competing Interests


Acknowledgments



Table6Structured

representatio

nof

thec

ompleted

OEK

Prob

lem

scenario

Con

tributors

Con

tent

Intent

Creditability

Prob

lem

IDProb

lem

name

Scenario

IDScenario

characteris

ticScenario

attribute

Experts

IDCr

itical

dimensio

n

Motion

combinatio

ndescrip

tion

Intent

descrip

tion

001

Theq

ualityof

weld

ingisno

tconstant

001

Theh

eadof

innerv

essel

Bend

ing

weld

ingargon

arcw

elding

001-L

KStep3

M3-M1P0-M4

-M2P

1-M4

Obtaintheh

igh

quality

weld

s100

002-YK

B003-CS

FStep3

M4-M2P

1times119899-M

4100

001-L

K002-YK

B003-CS

FStep5

M4P

2M3P

1Re

duce

the

pauseinthe

weld

ingprocess

100

001-L

K002-YK

B003-CS

FStep6

M2P

1times119899

Easy

toop

erate

andcontrol

strength

100

001-L

K002-YK

B003-CS

FArcingpo

int

14circle

Obtaintheh

igh

quality

weld

s80



Evaluation index

Values

Methods

Factor Attribute


and Winnips[7]


[15]


Elicitor





Informant




Problemdomain



Elicitationprocess




References



























































Volume 2014




Journal of











Journal of


Function Spaces






Algebra






























Action A 1

Action 3

Action n

Action 2

Start

End

Agent A

Action 1


Action A 2

Processed objects

Action B 1


Action A 3

Object 1

Object nAction B 3

ldquoGoa

lrdquoSp

ecifi

c con

tent

of g

oal

Spec

ific c

onte

nt o

f go

alSp

ecifi

c con

tent

of

goal

ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo

Spec

ific c

onte

nt

of g

oal

ldquoSub

goalrdquo

Spec

ific c

onte

nt

of g

oal

ldquoSub

goalrdquo

Spec

ific c

onte

nt o

f go

al

Subg

oal 1

Subg

oal 2

Subg

oal n

Object 2

Action A 3

Action A 3

Action A 3

Action B 1

Action A 3

Action B 3

Action B 3

Action B 3

Action B 3

Action N 1

Action N 3

Action N 1

Action N 3

Action N 3

Action N 3

Action N 3


ldquoSce

nario

fact

orsrdquo

Spec

ific f

acto

rs









cup119886isin119860119881119886 where 119881




119886


1198641 1198642 119864


into 119899 subsets of 1198641 1198642 119864




Action A 1

Action 3

Action n

Action 2

Start

End

Agent A

Action 1


Action A 2

Processed objects

Action B 1

Action A 3

Object 1

Object nAction B 3

ldquoGoa

lrdquoSp

ecifi

c con

tent

of g

oal

ldquoSub

goalrdquo

Spec

ific c

onte

nt o

f go

al

ldquoSub

goalrdquo

Spec

ific c

onte

nt o

f go

al

ldquoSub

goalrdquo

Spec

ific c

onte

nt

of g

oal

ldquoSub

goalrdquo

Spec

ific c

onte

nt

of g

oal

ldquoSub

goalrdquo

Spec

ific c

onte

nt o

f go

al

Subg

oal 1

Su

bgoa

l 2

Subg

oal n

Object 2

Action A 3

Action A 3

Action A 3

Action B 1

Action A 3

Action B 3

Action B 3

Action B 3

Action B 3

Action N 1

Action N 3

Action N 1

Action N 3

Action N 3

Action N 3

Action N 3


ldquoSce

nario

fact

orsrdquo

Spec

ific f

acto

rs


ldquoPro

duct

qua

lityrdquo

Spec

ific f

acto

rs

Parameter A 1

Parameter A 2

Parameter A n

Parameter C 1

Parameter C 2

Parameter C n

Parameter B 1

Parameter B 2

Parameter B n


ActionAgent

Messageaction



119862120573(119863) = ⋃

1minus119875119903(119885|119909119894)le120573

119909119894isin 119864 (119875) (4)


119862120573(119863) = ⋃

1minus119875119903(119885|119909119894)lt1minus120573

119909119894isin 119864 (119875) (5)


119885 sub 119864 (119863)

119875119903(119885 | 119909

119894) =

Card (119885 cap 119909119894)

Card (119883119894)

(6)


120574 (119875119863 120573) =

Card (⋃1minus119875119903(119885|119909119894)

119909119894isin 119864 (119875))

Card (119880) (7)










[119862]minus











119862 larr997888 119862 cup 119886 (9)





End



Then 119862 larr997888 119862 minus 119886 (12)

End For


119877 larr997888 119877 cup 119903 (13)


119866 larr997888 119866 minus ⋃

119903isin119877

[119903] (14)



If 119870 sube ⋃

119878isin(119877minus119903)

[119904] Then 119877 larr997888 119877 minus 119903 (15)

End For













welding gun M3P2



6 Case Study




62 Motion Modeling














② M3④ M1④ P2

③ P2

① M4

③ M4② G1

⑤ H

② M3

⑤ P0 ④ M1

⑤ P0 ④ M1

⑤ P0 ④ M1

⑤ P0














7 Discussion






M4

Position

Inspecting

Put

Start

Agent A

Grasp


M3

Processed objects

M3

No 1 pipe

M3

ldquoGoa

lrdquoPi

pes w

eldin

g

Num

ber 1

pip

e weld

ing

Weld

ing

prep

arat

ion

Weld

ing

Posit

ion

Weld

ing

mat

eria

ls pr

epar

atio

n

Subg

oal 2

Su

bgoa

l 1

Subg

oal 1

P2

M1

G0

G1

E2

P5

M3

P1

P1

M3

M1

G1

P2

M1

P1

P1

ldquoSub

goalrdquo

Qua

lity

insp

ectio

n

ldquoSub

goalrdquo

Fiel

d in

spec

tion

Subg

oal 1

Welding

M1

G0

D3

G1

M3

D3

M3

M3

D3

M3

E2

ldquoSce

nario

fact

orsrdquo

M

an-m

achi

ne in

tera

ctio

n

Hum

an-a

rtifa

cts

inte

ract

ion

ldquoSub

goalrdquo

Hum

an-to

ols

inte

ract

ion

The n

umbe

r of

rota

tions

Arc

ing

poin

t

ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo

Ges

ture

of h

oldi

ng

tool

Subg

oal 2

Su

bgoa

l 1

Subg

oal 1

Mod

e cho

osin

g

Subg

oal 3

D3 D3 D3

6 12 12


ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo





M4

Position

Inspecting

Put

Start

Agent A

Grasp


M3

ProcessedobjectsM3

Number 1

M3

ldquoGoa

lrdquoPi

pes w

eldin

g

ldquoSub

goalrdquo

Num

ber 1

pip

e weld

ing

ldquoSub

goalrdquo

Weld

ing

prep

arat

ion

ldquoSub

goalrdquo

Weld

ing

ldquoSub

goalrdquo

Posit

ion

ldquoSub

goalrdquo

Weld

ing

mat

eria

lspr

epar

atio

n

Subg

oal 2

Subg

oal 1

Subg

oal 1

P2

M1G0

G1

E2

P5

M3

P1

P1

M3

M1

G1

P2

M1

P1

P1

ldquoSub

goalrdquo

Qua

lity

insp

ectio

n

ldquoSub

goalrdquo

Fiel

d in

spec

tion

Subg

oal 1

WeldingM1G0

D3

G1

M3

D3

M3

M3

D3

M3

E2

ldquoSce

nario

fact

orsrdquo

Man

-mac

hine

inte

ract

ion

Hum

an-a

rtifa

cts

inte

ract

ion

Hum

an-to

ols

inte

ract

ion

The n

umbe

r of

rota

tions

Arc

ing

poin

tG

estu

re o

fho

ldin

g to

ol

Subg

oal 2

Subg

oal 1

Subg

oal 1

Mod

e cho

osin

g

Subg

oal 3

ldquoQua

lity

fact

orsrdquo

The q

ualit

y of

weld

ing

The s

urfa

ce q

ualit

yof

weld

ing

The c

olor

of

weld

ing

D3 D3 D3

6 12 12


Black Black Black


Well-distributed

Well-distributed

ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo



pipe



Table3Th

eresulto

fpreprocessin

g

IDStep1

Step2

Step3

Step4

Step5

Step6

Step7

Righth

and

Arcingpo

int

Then

umber

ofrotatio

nsMod

echoo

sing

Color

Surfa

cequ

ality

1M4P

2M3P

1times119899-M

3

M3-M1P0-

M4-M2P

1-M4

M3P

1M4P

2M4P

2M3P

1M2P

1times119899

M3P

1times119899-M

3M1P0times119899

14circle

8Com

binatio

nYellow

Well-

distr

ibuted

2M3P

2M1G

0times119899-M

3M4-M2P

1times119899-M

4Null

Null

M1P1times

119899M1G

0times119899-M

3M3P

1times119899

24circle

12Successio

nBlack

Well-

distr

ibuted

3M3P

2M1P1

times119899-M

3M4-M2P

1times119899-M

4Null

Null

M1P1times

119899M1G

0times119899-M

3M1P0times119899

24circle

12Successio

nBlack

Well-

distr

ibuted

4M3P

2M3P

1times119899-M

3M4-M2P

1times119899-M

4Null

Null

M2P

1times119899

M3P

1times119899-M

3M1P0times119899

14circle

12Com

binatio

nYello

wWell-

distr

ibuted

5M3P

2M1P1

times119899-M

3M4-M2P

1times119899-M

4Null

Null

M1P1times

119899M1G

0times119899-M

3M1P0times119899

14circle

12Successio

nBlack

Well-

distr

ibuted

6M3P

2M4P

1times119899-M

3M4-M2P

1times119899-M

4M3P

1M4P

2M4P

2M3P

1M2P

1times119899

M1G

0times119899-M

3M1P0times119899

14circle

8Com

binatio

nYellow

Non

-well-

distr

ibuted

7M3P

2M2P

1times119899-M

3M4-M2P

1times119899-M

4Null

Null

M1P1times

119899M1G

0times119899-M

3M1P0times119899

14circle

12Successio

nYello

wWell-

distr

ibuted

8M3P

5M3P

1times119899-M

3M4-M2P

1times119899-M

4M3P

1M4P

2M4P

2M3P

1M2P

1times119899

M4-M3P

1-M3

M3P

1times119899

14circle

12Com

binatio

nYello

wWell-

distr

ibuted

9M3P

2M2P

1times119899-M

3M3-M1P0-

M3

Null

Null

M1P1times

119899M1G

0times119899-M

3M3P

1times119899

14circle

12Successio

nBlack

Well-

distr

ibuted

10M2P

0M1G

0times119899-M

3M3-M1P0-

M3

Null

Null

M2P

1times119899

M4-M3P

1-M3

M3P

1times119899

24circle

11Successio

nBlack

Well-

distr

ibuted

11M2P

0M1G

0times119899-M

3M3-M1P0-

M3

Null

Null

M2P

1times119899

M1G

0times119899-M

3M3P

1times119899

24circle

12Successio

nBlack

Well-

distr

ibuted

12M3P

2M1G

0times119899-M

3

M3-M1P0-

M4-M2P

1-M4

Null

Null

M1P1times

119899M1G

0times119899-M

3M1P0times119899

24circle

6Com

binatio

nBlack

Non

-well-

distr

ibuted

13M2P

0M1G

0times119899-M

3M3-M1P0-

M3

Null

Null

M2P

1times119899

M1G

0times119899-M

3M3P

1times119899

24circle

12Successio

nBlack

Well-

distr

ibuted

14M4P

2M4P

1times119899-M

3M3-M1P0-

M3

M3P

1M4P

2M4P

2M3P

1M2P

1-M1P1

times119899

M2P

1times119899-M

3M3P

1times119899

24circle

16Com

binatio

nYello

wNon

-well-

distr

ibuted

15M3P

2M1G

0times119899-M

3M4-M2P

1times119899-M

4Null

Null

M2P

1times119899

M1G

0times119899-M

3M3P

1times119899

24circle

12Successio

nBlack

Well-

distr

ibuted

16M3P

5M3

times119899-M

3M4-M2P

1times119899-M

4M3P

1M4P

2M4P

2M3P

1M1P1times

119899M3times119899-M

3M1P0times119899

24circle

6Com

binatio

nBlack

Non

-well-

distr

ibuted

17M4M

3P2

M3

times119899-M

3M4-M2P

1times119899-M

4Null

Null

M1P1times

119899M3times119899-M

3M1P0times119899

34circle

6Com

binatio

nBlack

Non

-well-

distr

ibuted

18M3P

5M1G

0times119899-M

3M4-M2P

1times119899-M

4Null

Null

M2P

1-M1P1

times119899

M3times119899-M

3M1P0times119899

34circle

6Com

binatio

nBlack

Non

-well-

distr

ibuted




156

105

0

2

4

6

8

10

12

14

16

18





1190

313

0

200

400

600

800

1000

1200

1400


















8 Conclusion








Competing Interests


Acknowledgments



Table6Structured

representatio

nof

thec

ompleted

OEK

Prob

lem

scenario

Con

tributors

Con

tent

Intent

Creditability

Prob

lem

IDProb

lem

name

Scenario

IDScenario

characteris

ticScenario

attribute

Experts

IDCr

itical

dimensio

n

Motion

combinatio

ndescrip

tion

Intent

descrip

tion

001

Theq

ualityof

weld

ingisno

tconstant

001

Theh

eadof

innerv

essel

Bend

ing

weld

ingargon

arcw

elding

001-L

KStep3

M3-M1P0-M4

-M2P

1-M4

Obtaintheh

igh

quality

weld

s100

002-YK

B003-CS

FStep3

M4-M2P

1times119899-M

4100

001-L

K002-YK

B003-CS

FStep5

M4P

2M3P

1Re

duce

the

pauseinthe

weld

ingprocess

100

001-L

K002-YK

B003-CS

FStep6

M2P

1times119899

Easy

toop

erate

andcontrol

strength

100

001-L

K002-YK

B003-CS

FArcingpo

int

14circle

Obtaintheh

igh

quality

weld

s80



Evaluation index

Values

Methods

Factor Attribute


and Winnips[7]


[15]


Elicitor





Informant




Problemdomain



Elicitationprocess




References



























































Volume 2014




Journal of











Journal of


Function Spaces






Algebra










Action A 1

Action 3

Action n

Action 2

Start

End

Agent A

Action 1


Action A 2

Processed objects

Action B 1

Action A 3

Object 1

Object nAction B 3

ldquoGoa

lrdquoSp

ecifi

c con

tent

of g

oal

ldquoSub

goalrdquo

Spec

ific c

onte

nt o

f go

al

ldquoSub

goalrdquo

Spec

ific c

onte

nt o

f go

al

ldquoSub

goalrdquo

Spec

ific c

onte

nt

of g

oal

ldquoSub

goalrdquo

Spec

ific c

onte

nt

of g

oal

ldquoSub

goalrdquo

Spec

ific c

onte

nt o

f go

al

Subg

oal 1

Su

bgoa

l 2

Subg

oal n

Object 2

Action A 3

Action A 3

Action A 3

Action B 1

Action A 3

Action B 3

Action B 3

Action B 3

Action B 3

Action N 1

Action N 3

Action N 1

Action N 3

Action N 3

Action N 3

Action N 3


ldquoSce

nario

fact

orsrdquo

Spec

ific f

acto

rs


ldquoPro

duct

qua

lityrdquo

Spec

ific f

acto

rs

Parameter A 1

Parameter A 2

Parameter A n

Parameter C 1

Parameter C 2

Parameter C n

Parameter B 1

Parameter B 2

Parameter B n


ActionAgent

Messageaction



119862120573(119863) = ⋃

1minus119875119903(119885|119909119894)le120573

119909119894isin 119864 (119875) (4)


119862120573(119863) = ⋃

1minus119875119903(119885|119909119894)lt1minus120573

119909119894isin 119864 (119875) (5)


119885 sub 119864 (119863)

119875119903(119885 | 119909

119894) =

Card (119885 cap 119909119894)

Card (119883119894)

(6)


120574 (119875119863 120573) =

Card (⋃1minus119875119903(119885|119909119894)

119909119894isin 119864 (119875))

Card (119880) (7)










[119862]minus











119862 larr997888 119862 cup 119886 (9)





End



Then 119862 larr997888 119862 minus 119886 (12)

End For


119877 larr997888 119877 cup 119903 (13)


119866 larr997888 119866 minus ⋃

119903isin119877

[119903] (14)



If 119870 sube ⋃

119878isin(119877minus119903)

[119904] Then 119877 larr997888 119877 minus 119903 (15)

End For













welding gun M3P2



6 Case Study




62 Motion Modeling














② M3④ M1④ P2

③ P2

① M4

③ M4② G1

⑤ H

② M3

⑤ P0 ④ M1

⑤ P0 ④ M1

⑤ P0 ④ M1

⑤ P0














7 Discussion






M4

Position

Inspecting

Put

Start

Agent A

Grasp


M3

Processed objects

M3

No 1 pipe

M3

ldquoGoa

lrdquoPi

pes w

eldin

g

Num

ber 1

pip

e weld

ing

Weld

ing

prep

arat

ion

Weld

ing

Posit

ion

Weld

ing

mat

eria

ls pr

epar

atio

n

Subg

oal 2

Su

bgoa

l 1

Subg

oal 1

P2

M1

G0

G1

E2

P5

M3

P1

P1

M3

M1

G1

P2

M1

P1

P1

ldquoSub

goalrdquo

Qua

lity

insp

ectio

n

ldquoSub

goalrdquo

Fiel

d in

spec

tion

Subg

oal 1

Welding

M1

G0

D3

G1

M3

D3

M3

M3

D3

M3

E2

ldquoSce

nario

fact

orsrdquo

M

an-m

achi

ne in

tera

ctio

n

Hum

an-a

rtifa

cts

inte

ract

ion

ldquoSub

goalrdquo

Hum

an-to

ols

inte

ract

ion

The n

umbe

r of

rota

tions

Arc

ing

poin

t

ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo

Ges

ture

of h

oldi

ng

tool

Subg

oal 2

Su

bgoa

l 1

Subg

oal 1

Mod

e cho

osin

g

Subg

oal 3

D3 D3 D3

6 12 12


ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo





M4

Position

Inspecting

Put

Start

Agent A

Grasp


M3

ProcessedobjectsM3

Number 1

M3

ldquoGoa

lrdquoPi

pes w

eldin

g

ldquoSub

goalrdquo

Num

ber 1

pip

e weld

ing

ldquoSub

goalrdquo

Weld

ing

prep

arat

ion

ldquoSub

goalrdquo

Weld

ing

ldquoSub

goalrdquo

Posit

ion

ldquoSub

goalrdquo

Weld

ing

mat

eria

lspr

epar

atio

n

Subg

oal 2

Subg

oal 1

Subg

oal 1

P2

M1G0

G1

E2

P5

M3

P1

P1

M3

M1

G1

P2

M1

P1

P1

ldquoSub

goalrdquo

Qua

lity

insp

ectio

n

ldquoSub

goalrdquo

Fiel

d in

spec

tion

Subg

oal 1

WeldingM1G0

D3

G1

M3

D3

M3

M3

D3

M3

E2

ldquoSce

nario

fact

orsrdquo

Man

-mac

hine

inte

ract

ion

Hum

an-a

rtifa

cts

inte

ract

ion

Hum

an-to

ols

inte

ract

ion

The n

umbe

r of

rota

tions

Arc

ing

poin

tG

estu

re o

fho

ldin

g to

ol

Subg

oal 2

Subg

oal 1

Subg

oal 1

Mod

e cho

osin

g

Subg

oal 3

ldquoQua

lity

fact

orsrdquo

The q

ualit

y of

weld

ing

The s

urfa

ce q

ualit

yof

weld

ing

The c

olor

of

weld

ing

D3 D3 D3

6 12 12


Black Black Black


Well-distributed

Well-distributed

ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo



pipe



Table3Th

eresulto

fpreprocessin

g

IDStep1

Step2

Step3

Step4

Step5

Step6

Step7

Righth

and

Arcingpo

int

Then

umber

ofrotatio

nsMod

echoo

sing

Color

Surfa

cequ

ality

1M4P

2M3P

1times119899-M

3

M3-M1P0-

M4-M2P

1-M4

M3P

1M4P

2M4P

2M3P

1M2P

1times119899

M3P

1times119899-M

3M1P0times119899

14circle

8Com

binatio

nYellow

Well-

distr

ibuted

2M3P

2M1G

0times119899-M

3M4-M2P

1times119899-M

4Null

Null

M1P1times

119899M1G

0times119899-M

3M3P

1times119899

24circle

12Successio

nBlack

Well-

distr

ibuted

3M3P

2M1P1

times119899-M

3M4-M2P

1times119899-M

4Null

Null

M1P1times

119899M1G

0times119899-M

3M1P0times119899

24circle

12Successio

nBlack

Well-

distr

ibuted

4M3P

2M3P

1times119899-M

3M4-M2P

1times119899-M

4Null

Null

M2P

1times119899

M3P

1times119899-M

3M1P0times119899

14circle

12Com

binatio

nYello

wWell-

distr

ibuted

5M3P

2M1P1

times119899-M

3M4-M2P

1times119899-M

4Null

Null

M1P1times

119899M1G

0times119899-M

3M1P0times119899

14circle

12Successio

nBlack

Well-

distr

ibuted

6M3P

2M4P

1times119899-M

3M4-M2P

1times119899-M

4M3P

1M4P

2M4P

2M3P

1M2P

1times119899

M1G

0times119899-M

3M1P0times119899

14circle

8Com

binatio

nYellow

Non

-well-

distr

ibuted

7M3P

2M2P

1times119899-M

3M4-M2P

1times119899-M

4Null

Null

M1P1times

119899M1G

0times119899-M

3M1P0times119899

14circle

12Successio

nYello

wWell-

distr

ibuted

8M3P

5M3P

1times119899-M

3M4-M2P

1times119899-M

4M3P

1M4P

2M4P

2M3P

1M2P

1times119899

M4-M3P

1-M3

M3P

1times119899

14circle

12Com

binatio

nYello

wWell-

distr

ibuted

9M3P

2M2P

1times119899-M

3M3-M1P0-

M3

Null

Null

M1P1times

119899M1G

0times119899-M

3M3P

1times119899

14circle

12Successio

nBlack

Well-

distr

ibuted

10M2P

0M1G

0times119899-M

3M3-M1P0-

M3

Null

Null

M2P

1times119899

M4-M3P

1-M3

M3P

1times119899

24circle

11Successio

nBlack

Well-

distr

ibuted

11M2P

0M1G

0times119899-M

3M3-M1P0-

M3

Null

Null

M2P

1times119899

M1G

0times119899-M

3M3P

1times119899

24circle

12Successio

nBlack

Well-

distr

ibuted

12M3P

2M1G

0times119899-M

3

M3-M1P0-

M4-M2P

1-M4

Null

Null

M1P1times

119899M1G

0times119899-M

3M1P0times119899

24circle

6Com

binatio

nBlack

Non

-well-

distr

ibuted

13M2P

0M1G

0times119899-M

3M3-M1P0-

M3

Null

Null

M2P

1times119899

M1G

0times119899-M

3M3P

1times119899

24circle

12Successio

nBlack

Well-

distr

ibuted

14M4P

2M4P

1times119899-M

3M3-M1P0-

M3

M3P

1M4P

2M4P

2M3P

1M2P

1-M1P1

times119899

M2P

1times119899-M

3M3P

1times119899

24circle

16Com

binatio

nYello

wNon

-well-

distr

ibuted

15M3P

2M1G

0times119899-M

3M4-M2P

1times119899-M

4Null

Null

M2P

1times119899

M1G

0times119899-M

3M3P

1times119899

24circle

12Successio

nBlack

Well-

distr

ibuted

16M3P

5M3

times119899-M

3M4-M2P

1times119899-M

4M3P

1M4P

2M4P

2M3P

1M1P1times

119899M3times119899-M

3M1P0times119899

24circle

6Com

binatio

nBlack

Non

-well-

distr

ibuted

17M4M

3P2

M3

times119899-M

3M4-M2P

1times119899-M

4Null

Null

M1P1times

119899M3times119899-M

3M1P0times119899

34circle

6Com

binatio

nBlack

Non

-well-

distr

ibuted

18M3P

5M1G

0times119899-M

3M4-M2P

1times119899-M

4Null

Null

M2P

1-M1P1

times119899

M3times119899-M

3M1P0times119899

34circle

6Com

binatio

nBlack

Non

-well-

distr

ibuted




156

105

0

2

4

6

8

10

12

14

16

18





1190

313

0

200

400

600

800

1000

1200

1400


















8 Conclusion








Competing Interests


Acknowledgments



Table6Structured

representatio

nof

thec

ompleted

OEK

Prob

lem

scenario

Con

tributors

Con

tent

Intent

Creditability

Prob

lem

IDProb

lem

name

Scenario

IDScenario

characteris

ticScenario

attribute

Experts

IDCr

itical

dimensio

n

Motion

combinatio

ndescrip

tion

Intent

descrip

tion

001

Theq

ualityof

weld

ingisno

tconstant

001

Theh

eadof

innerv

essel

Bend

ing

weld

ingargon

arcw

elding

001-L

KStep3

M3-M1P0-M4

-M2P

1-M4

Obtaintheh

igh

quality

weld

s100

002-YK

B003-CS

FStep3

M4-M2P

1times119899-M

4100

001-L

K002-YK

B003-CS

FStep5

M4P

2M3P

1Re

duce

the

pauseinthe

weld

ingprocess

100

001-L

K002-YK

B003-CS

FStep6

M2P

1times119899

Easy

toop

erate

andcontrol

strength

100

001-L

K002-YK

B003-CS

FArcingpo

int

14circle

Obtaintheh

igh

quality

weld

s80



Evaluation index

Values

Methods

Factor Attribute


and Winnips[7]


[15]


Elicitor





Informant




Problemdomain



Elicitationprocess




References



























































Volume 2014




Journal of











Journal of


Function Spaces






Algebra


















[119862]minus











119862 larr997888 119862 cup 119886 (9)





End



Then 119862 larr997888 119862 minus 119886 (12)

End For


119877 larr997888 119877 cup 119903 (13)


119866 larr997888 119866 minus ⋃

119903isin119877

[119903] (14)



If 119870 sube ⋃

119878isin(119877minus119903)

[119904] Then 119877 larr997888 119877 minus 119903 (15)

End For













welding gun M3P2



6 Case Study




62 Motion Modeling














② M3④ M1④ P2

③ P2

① M4

③ M4② G1

⑤ H

② M3

⑤ P0 ④ M1

⑤ P0 ④ M1

⑤ P0 ④ M1

⑤ P0














7 Discussion






M4

Position

Inspecting

Put

Start

Agent A

Grasp


M3

Processed objects

M3

No 1 pipe

M3

ldquoGoa

lrdquoPi

pes w

eldin

g

Num

ber 1

pip

e weld

ing

Weld

ing

prep

arat

ion

Weld

ing

Posit

ion

Weld

ing

mat

eria

ls pr

epar

atio

n

Subg

oal 2

Su

bgoa

l 1

Subg

oal 1

P2

M1

G0

G1

E2

P5

M3

P1

P1

M3

M1

G1

P2

M1

P1

P1

ldquoSub

goalrdquo

Qua

lity

insp

ectio

n

ldquoSub

goalrdquo

Fiel

d in

spec

tion

Subg

oal 1

Welding

M1

G0

D3

G1

M3

D3

M3

M3

D3

M3

E2

ldquoSce

nario

fact

orsrdquo

M

an-m

achi

ne in

tera

ctio

n

Hum

an-a

rtifa

cts

inte

ract

ion

ldquoSub

goalrdquo

Hum

an-to

ols

inte

ract

ion

The n

umbe

r of

rota

tions

Arc

ing

poin

t

ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo

Ges

ture

of h

oldi

ng

tool

Subg

oal 2

Su

bgoa

l 1

Subg

oal 1

Mod

e cho

osin

g

Subg

oal 3

D3 D3 D3

6 12 12


ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo





M4

Position

Inspecting

Put

Start

Agent A

Grasp


M3

ProcessedobjectsM3

Number 1

M3

ldquoGoa

lrdquoPi

pes w

eldin

g

ldquoSub

goalrdquo

Num

ber 1

pip

e weld

ing

ldquoSub

goalrdquo

Weld

ing

prep

arat

ion

ldquoSub

goalrdquo

Weld

ing

ldquoSub

goalrdquo

Posit

ion

ldquoSub

goalrdquo

Weld

ing

mat

eria

lspr

epar

atio

n

Subg

oal 2

Subg

oal 1

Subg

oal 1

P2

M1G0

G1

E2

P5

M3

P1

P1

M3

M1

G1

P2

M1

P1

P1

ldquoSub

goalrdquo

Qua

lity

insp

ectio

n

ldquoSub

goalrdquo

Fiel

d in

spec

tion

Subg

oal 1

WeldingM1G0

D3

G1

M3

D3

M3

M3

D3

M3

E2

ldquoSce

nario

fact

orsrdquo

Man

-mac

hine

inte

ract

ion

Hum

an-a

rtifa

cts

inte

ract

ion

Hum

an-to

ols

inte

ract

ion

The n

umbe

r of

rota

tions

Arc

ing

poin

tG

estu

re o

fho

ldin

g to

ol

Subg

oal 2

Subg

oal 1

Subg

oal 1

Mod

e cho

osin

g

Subg

oal 3

ldquoQua

lity

fact

orsrdquo

The q

ualit

y of

weld

ing

The s

urfa

ce q

ualit

yof

weld

ing

The c

olor

of

weld

ing

D3 D3 D3

6 12 12


Black Black Black


Well-distributed

Well-distributed

ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo



pipe



Table3Th

eresulto

fpreprocessin

g

IDStep1

Step2

Step3

Step4

Step5

Step6

Step7

Righth

and

Arcingpo

int

Then

umber

ofrotatio

nsMod

echoo

sing

Color

Surfa

cequ

ality

1M4P

2M3P

1times119899-M

3

M3-M1P0-

M4-M2P

1-M4

M3P

1M4P

2M4P

2M3P

1M2P

1times119899

M3P

1times119899-M

3M1P0times119899

14circle

8Com

binatio

nYellow

Well-

distr

ibuted

2M3P

2M1G

0times119899-M

3M4-M2P

1times119899-M

4Null

Null

M1P1times

119899M1G

0times119899-M

3M3P

1times119899

24circle

12Successio

nBlack

Well-

distr

ibuted

3M3P

2M1P1

times119899-M

3M4-M2P

1times119899-M

4Null

Null

M1P1times

119899M1G

0times119899-M

3M1P0times119899

24circle

12Successio

nBlack

Well-

distr

ibuted

4M3P

2M3P

1times119899-M

3M4-M2P

1times119899-M

4Null

Null

M2P

1times119899

M3P

1times119899-M

3M1P0times119899

14circle

12Com

binatio

nYello

wWell-

distr

ibuted

5M3P

2M1P1

times119899-M

3M4-M2P

1times119899-M

4Null

Null

M1P1times

119899M1G

0times119899-M

3M1P0times119899

14circle

12Successio

nBlack

Well-

distr

ibuted

6M3P

2M4P

1times119899-M

3M4-M2P

1times119899-M

4M3P

1M4P

2M4P

2M3P

1M2P

1times119899

M1G

0times119899-M

3M1P0times119899

14circle

8Com

binatio

nYellow

Non

-well-

distr

ibuted

7M3P

2M2P

1times119899-M

3M4-M2P

1times119899-M

4Null

Null

M1P1times

119899M1G

0times119899-M

3M1P0times119899

14circle

12Successio

nYello

wWell-

distr

ibuted

8M3P

5M3P

1times119899-M

3M4-M2P

1times119899-M

4M3P

1M4P

2M4P

2M3P

1M2P

1times119899

M4-M3P

1-M3

M3P

1times119899

14circle

12Com

binatio

nYello

wWell-

distr

ibuted

9M3P

2M2P

1times119899-M

3M3-M1P0-

M3

Null

Null

M1P1times

119899M1G

0times119899-M

3M3P

1times119899

14circle

12Successio

nBlack

Well-

distr

ibuted

10M2P

0M1G

0times119899-M

3M3-M1P0-

M3

Null

Null

M2P

1times119899

M4-M3P

1-M3

M3P

1times119899

24circle

11Successio

nBlack

Well-

distr

ibuted

11M2P

0M1G

0times119899-M

3M3-M1P0-

M3

Null

Null

M2P

1times119899

M1G

0times119899-M

3M3P

1times119899

24circle

12Successio

nBlack

Well-

distr

ibuted

12M3P

2M1G

0times119899-M

3

M3-M1P0-

M4-M2P

1-M4

Null

Null

M1P1times

119899M1G

0times119899-M

3M1P0times119899

24circle

6Com

binatio

nBlack

Non

-well-

distr

ibuted

13M2P

0M1G

0times119899-M

3M3-M1P0-

M3

Null

Null

M2P

1times119899

M1G

0times119899-M

3M3P

1times119899

24circle

12Successio

nBlack

Well-

distr

ibuted

14M4P

2M4P

1times119899-M

3M3-M1P0-

M3

M3P

1M4P

2M4P

2M3P

1M2P

1-M1P1

times119899

M2P

1times119899-M

3M3P

1times119899

24circle

16Com

binatio

nYello

wNon

-well-

distr

ibuted

15M3P

2M1G

0times119899-M

3M4-M2P

1times119899-M

4Null

Null

M2P

1times119899

M1G

0times119899-M

3M3P

1times119899

24circle

12Successio

nBlack

Well-

distr

ibuted

16M3P

5M3

times119899-M

3M4-M2P

1times119899-M

4M3P

1M4P

2M4P

2M3P

1M1P1times

119899M3times119899-M

3M1P0times119899

24circle

6Com

binatio

nBlack

Non

-well-

distr

ibuted

17M4M

3P2

M3

times119899-M

3M4-M2P

1times119899-M

4Null

Null

M1P1times

119899M3times119899-M

3M1P0times119899

34circle

6Com

binatio

nBlack

Non

-well-

distr

ibuted

18M3P

5M1G

0times119899-M

3M4-M2P

1times119899-M

4Null

Null

M2P

1-M1P1

times119899

M3times119899-M

3M1P0times119899

34circle

6Com

binatio

nBlack

Non

-well-

distr

ibuted




156

105

0

2

4

6

8

10

12

14

16

18





1190

313

0

200

400

600

800

1000

1200

1400


















8 Conclusion








Competing Interests


Acknowledgments



Table6Structured

representatio

nof

thec

ompleted

OEK

Prob

lem

scenario

Con

tributors

Con

tent

Intent

Creditability

Prob

lem

IDProb

lem

name

Scenario

IDScenario

characteris

ticScenario

attribute

Experts

IDCr

itical

dimensio

n

Motion

combinatio

ndescrip

tion

Intent

descrip

tion

001

Theq

ualityof

weld

ingisno

tconstant

001

Theh

eadof

innerv

essel

Bend

ing

weld

ingargon

arcw

elding

001-L

KStep3

M3-M1P0-M4

-M2P

1-M4

Obtaintheh

igh

quality

weld

s100

002-YK

B003-CS

FStep3

M4-M2P

1times119899-M

4100

001-L

K002-YK

B003-CS

FStep5

M4P

2M3P

1Re

duce

the

pauseinthe

weld

ingprocess

100

001-L

K002-YK

B003-CS

FStep6

M2P

1times119899

Easy

toop

erate

andcontrol

strength

100

001-L

K002-YK

B003-CS

FArcingpo

int

14circle

Obtaintheh

igh

quality

weld

s80



Evaluation index

Values

Methods

Factor Attribute


and Winnips[7]


[15]


Elicitor





Informant




Problemdomain



Elicitationprocess




References



























































Volume 2014




Journal of











Journal of


Function Spaces






Algebra

















welding gun M3P2



6 Case Study




62 Motion Modeling














② M3④ M1④ P2

③ P2

① M4

③ M4② G1

⑤ H

② M3

⑤ P0 ④ M1

⑤ P0 ④ M1

⑤ P0 ④ M1

⑤ P0














7 Discussion






M4

Position

Inspecting

Put

Start

Agent A

Grasp


M3

Processed objects

M3

No 1 pipe

M3

ldquoGoa

lrdquoPi

pes w

eldin

g

Num

ber 1

pip

e weld

ing

Weld

ing

prep

arat

ion

Weld

ing

Posit

ion

Weld

ing

mat

eria

ls pr

epar

atio

n

Subg

oal 2

Su

bgoa

l 1

Subg

oal 1

P2

M1

G0

G1

E2

P5

M3

P1

P1

M3

M1

G1

P2

M1

P1

P1

ldquoSub

goalrdquo

Qua

lity

insp

ectio

n

ldquoSub

goalrdquo

Fiel

d in

spec

tion

Subg

oal 1

Welding

M1

G0

D3

G1

M3

D3

M3

M3

D3

M3

E2

ldquoSce

nario

fact

orsrdquo

M

an-m

achi

ne in

tera

ctio

n

Hum

an-a

rtifa

cts

inte

ract

ion

ldquoSub

goalrdquo

Hum

an-to

ols

inte

ract

ion

The n

umbe

r of

rota

tions

Arc

ing

poin

t

ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo

Ges

ture

of h

oldi

ng

tool

Subg

oal 2

Su

bgoa

l 1

Subg

oal 1

Mod

e cho

osin

g

Subg

oal 3

D3 D3 D3

6 12 12


ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo





M4

Position

Inspecting

Put

Start

Agent A

Grasp


M3

ProcessedobjectsM3

Number 1

M3

ldquoGoa

lrdquoPi

pes w

eldin

g

ldquoSub

goalrdquo

Num

ber 1

pip

e weld

ing

ldquoSub

goalrdquo

Weld

ing

prep

arat

ion

ldquoSub

goalrdquo

Weld

ing

ldquoSub

goalrdquo

Posit

ion

ldquoSub

goalrdquo

Weld

ing

mat

eria

lspr

epar

atio

n

Subg

oal 2

Subg

oal 1

Subg

oal 1

P2

M1G0

G1

E2

P5

M3

P1

P1

M3

M1

G1

P2

M1

P1

P1

ldquoSub

goalrdquo

Qua

lity

insp

ectio

n

ldquoSub

goalrdquo

Fiel

d in

spec

tion

Subg

oal 1

WeldingM1G0

D3

G1

M3

D3

M3

M3

D3

M3

E2

ldquoSce

nario

fact

orsrdquo

Man

-mac

hine

inte

ract

ion

Hum

an-a

rtifa

cts

inte

ract

ion

Hum

an-to

ols

inte

ract

ion

The n

umbe

r of

rota

tions

Arc

ing

poin

tG

estu

re o

fho

ldin

g to

ol

Subg

oal 2

Subg

oal 1

Subg

oal 1

Mod

e cho

osin

g

Subg

oal 3

ldquoQua

lity

fact

orsrdquo

The q

ualit

y of

weld

ing

The s

urfa

ce q

ualit

yof

weld

ing

The c

olor

of

weld

ing

D3 D3 D3

6 12 12


Black Black Black


Well-distributed

Well-distributed

ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo



pipe



Table3Th

eresulto

fpreprocessin

g

IDStep1

Step2

Step3

Step4

Step5

Step6

Step7

Righth

and

Arcingpo

int

Then

umber

ofrotatio

nsMod

echoo

sing

Color

Surfa

cequ

ality

1M4P

2M3P

1times119899-M

3

M3-M1P0-

M4-M2P

1-M4

M3P

1M4P

2M4P

2M3P

1M2P

1times119899

M3P

1times119899-M

3M1P0times119899

14circle

8Com

binatio

nYellow

Well-

distr

ibuted

2M3P

2M1G

0times119899-M

3M4-M2P

1times119899-M

4Null

Null

M1P1times

119899M1G

0times119899-M

3M3P

1times119899

24circle

12Successio

nBlack

Well-

distr

ibuted

3M3P

2M1P1

times119899-M

3M4-M2P

1times119899-M

4Null

Null

M1P1times

119899M1G

0times119899-M

3M1P0times119899

24circle

12Successio

nBlack

Well-

distr

ibuted

4M3P

2M3P

1times119899-M

3M4-M2P

1times119899-M

4Null

Null

M2P

1times119899

M3P

1times119899-M

3M1P0times119899

14circle

12Com

binatio

nYello

wWell-

distr

ibuted

5M3P

2M1P1

times119899-M

3M4-M2P

1times119899-M

4Null

Null

M1P1times

119899M1G

0times119899-M

3M1P0times119899

14circle

12Successio

nBlack

Well-

distr

ibuted

6M3P

2M4P

1times119899-M

3M4-M2P

1times119899-M

4M3P

1M4P

2M4P

2M3P

1M2P

1times119899

M1G

0times119899-M

3M1P0times119899

14circle

8Com

binatio

nYellow

Non

-well-

distr

ibuted

7M3P

2M2P

1times119899-M

3M4-M2P

1times119899-M

4Null

Null

M1P1times

119899M1G

0times119899-M

3M1P0times119899

14circle

12Successio

nYello

wWell-

distr

ibuted

8M3P

5M3P

1times119899-M

3M4-M2P

1times119899-M

4M3P

1M4P

2M4P

2M3P

1M2P

1times119899

M4-M3P

1-M3

M3P

1times119899

14circle

12Com

binatio

nYello

wWell-

distr

ibuted

9M3P

2M2P

1times119899-M

3M3-M1P0-

M3

Null

Null

M1P1times

119899M1G

0times119899-M

3M3P

1times119899

14circle

12Successio

nBlack

Well-

distr

ibuted

10M2P

0M1G

0times119899-M

3M3-M1P0-

M3

Null

Null

M2P

1times119899

M4-M3P

1-M3

M3P

1times119899

24circle

11Successio

nBlack

Well-

distr

ibuted

11M2P

0M1G

0times119899-M

3M3-M1P0-

M3

Null

Null

M2P

1times119899

M1G

0times119899-M

3M3P

1times119899

24circle

12Successio

nBlack

Well-

distr

ibuted

12M3P

2M1G

0times119899-M

3

M3-M1P0-

M4-M2P

1-M4

Null

Null

M1P1times

119899M1G

0times119899-M

3M1P0times119899

24circle

6Com

binatio

nBlack

Non

-well-

distr

ibuted

13M2P

0M1G

0times119899-M

3M3-M1P0-

M3

Null

Null

M2P

1times119899

M1G

0times119899-M

3M3P

1times119899

24circle

12Successio

nBlack

Well-

distr

ibuted

14M4P

2M4P

1times119899-M

3M3-M1P0-

M3

M3P

1M4P

2M4P

2M3P

1M2P

1-M1P1

times119899

M2P

1times119899-M

3M3P

1times119899

24circle

16Com

binatio

nYello

wNon

-well-

distr

ibuted

15M3P

2M1G

0times119899-M

3M4-M2P

1times119899-M

4Null

Null

M2P

1times119899

M1G

0times119899-M

3M3P

1times119899

24circle

12Successio

nBlack

Well-

distr

ibuted

16M3P

5M3

times119899-M

3M4-M2P

1times119899-M

4M3P

1M4P

2M4P

2M3P

1M1P1times

119899M3times119899-M

3M1P0times119899

24circle

6Com

binatio

nBlack

Non

-well-

distr

ibuted

17M4M

3P2

M3

times119899-M

3M4-M2P

1times119899-M

4Null

Null

M1P1times

119899M3times119899-M

3M1P0times119899

34circle

6Com

binatio

nBlack

Non

-well-

distr

ibuted

18M3P

5M1G

0times119899-M

3M4-M2P

1times119899-M

4Null

Null

M2P

1-M1P1

times119899

M3times119899-M

3M1P0times119899

34circle

6Com

binatio

nBlack

Non

-well-

distr

ibuted




156

105

0

2

4

6

8

10

12

14

16

18





1190

313

0

200

400

600

800

1000

1200

1400


















8 Conclusion








Competing Interests


Acknowledgments



Table6Structured

representatio

nof

thec

ompleted

OEK

Prob

lem

scenario

Con

tributors

Con

tent

Intent

Creditability

Prob

lem

IDProb

lem

name

Scenario

IDScenario

characteris

ticScenario

attribute

Experts

IDCr

itical

dimensio

n

Motion

combinatio

ndescrip

tion

Intent

descrip

tion

001

Theq

ualityof

weld

ingisno

tconstant

001

Theh

eadof

innerv

essel

Bend

ing

weld

ingargon

arcw

elding

001-L

KStep3

M3-M1P0-M4

-M2P

1-M4

Obtaintheh

igh

quality

weld

s100

002-YK

B003-CS

FStep3

M4-M2P

1times119899-M

4100

001-L

K002-YK

B003-CS

FStep5

M4P

2M3P

1Re

duce

the

pauseinthe

weld

ingprocess

100

001-L

K002-YK

B003-CS

FStep6

M2P

1times119899

Easy

toop

erate

andcontrol

strength

100

001-L

K002-YK

B003-CS

FArcingpo

int

14circle

Obtaintheh

igh

quality

weld

s80



Evaluation index

Values

Methods

Factor Attribute


and Winnips[7]


[15]


Elicitor





Informant




Problemdomain



Elicitationprocess




References



























































Volume 2014




Journal of











Journal of


Function Spaces






Algebra











② M3④ M1④ P2

③ P2

① M4

③ M4② G1

⑤ H

② M3

⑤ P0 ④ M1

⑤ P0 ④ M1

⑤ P0 ④ M1

⑤ P0














7 Discussion






M4

Position

Inspecting

Put

Start

Agent A

Grasp


M3

Processed objects

M3

No 1 pipe

M3

ldquoGoa

lrdquoPi

pes w

eldin

g

Num

ber 1

pip

e weld

ing

Weld

ing

prep

arat

ion

Weld

ing

Posit

ion

Weld

ing

mat

eria

ls pr

epar

atio

n

Subg

oal 2

Su

bgoa

l 1

Subg

oal 1

P2

M1

G0

G1

E2

P5

M3

P1

P1

M3

M1

G1

P2

M1

P1

P1

ldquoSub

goalrdquo

Qua

lity

insp

ectio

n

ldquoSub

goalrdquo

Fiel

d in

spec

tion

Subg

oal 1

Welding

M1

G0

D3

G1

M3

D3

M3

M3

D3

M3

E2

ldquoSce

nario

fact

orsrdquo

M

an-m

achi

ne in

tera

ctio

n

Hum

an-a

rtifa

cts

inte

ract

ion

ldquoSub

goalrdquo

Hum

an-to

ols

inte

ract

ion

The n

umbe

r of

rota

tions

Arc

ing

poin

t

ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo

Ges

ture

of h

oldi

ng

tool

Subg

oal 2

Su

bgoa

l 1

Subg

oal 1

Mod

e cho

osin

g

Subg

oal 3

D3 D3 D3

6 12 12


ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo





M4

Position

Inspecting

Put

Start

Agent A

Grasp


M3

ProcessedobjectsM3

Number 1

M3

ldquoGoa

lrdquoPi

pes w

eldin

g

ldquoSub

goalrdquo

Num

ber 1

pip

e weld

ing

ldquoSub

goalrdquo

Weld

ing

prep

arat

ion

ldquoSub

goalrdquo

Weld

ing

ldquoSub

goalrdquo

Posit

ion

ldquoSub

goalrdquo

Weld

ing

mat

eria

lspr

epar

atio

n

Subg

oal 2

Subg

oal 1

Subg

oal 1

P2

M1G0

G1

E2

P5

M3

P1

P1

M3

M1

G1

P2

M1

P1

P1

ldquoSub

goalrdquo

Qua

lity

insp

ectio

n

ldquoSub

goalrdquo

Fiel

d in

spec

tion

Subg

oal 1

WeldingM1G0

D3

G1

M3

D3

M3

M3

D3

M3

E2

ldquoSce

nario

fact

orsrdquo

Man

-mac

hine

inte

ract

ion

Hum

an-a

rtifa

cts

inte

ract

ion

Hum

an-to

ols

inte

ract

ion

The n

umbe

r of

rota

tions

Arc

ing

poin

tG

estu

re o

fho

ldin

g to

ol

Subg

oal 2

Subg

oal 1

Subg

oal 1

Mod

e cho

osin

g

Subg

oal 3

ldquoQua

lity

fact

orsrdquo

The q

ualit

y of

weld

ing

The s

urfa

ce q

ualit

yof

weld

ing

The c

olor

of

weld

ing

D3 D3 D3

6 12 12


Black Black Black


Well-distributed

Well-distributed

ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo



pipe



Table3Th

eresulto

fpreprocessin

g

IDStep1

Step2

Step3

Step4

Step5

Step6

Step7

Righth

and

Arcingpo

int

Then

umber

ofrotatio

nsMod

echoo

sing

Color

Surfa

cequ

ality

1M4P

2M3P

1times119899-M

3

M3-M1P0-

M4-M2P

1-M4

M3P

1M4P

2M4P

2M3P

1M2P

1times119899

M3P

1times119899-M

3M1P0times119899

14circle

8Com

binatio

nYellow

Well-

distr

ibuted

2M3P

2M1G

0times119899-M

3M4-M2P

1times119899-M

4Null

Null

M1P1times

119899M1G

0times119899-M

3M3P

1times119899

24circle

12Successio

nBlack

Well-

distr

ibuted

3M3P

2M1P1

times119899-M

3M4-M2P

1times119899-M

4Null

Null

M1P1times

119899M1G

0times119899-M

3M1P0times119899

24circle

12Successio

nBlack

Well-

distr

ibuted

4M3P

2M3P

1times119899-M

3M4-M2P

1times119899-M

4Null

Null

M2P

1times119899

M3P

1times119899-M

3M1P0times119899

14circle

12Com

binatio

nYello

wWell-

distr

ibuted

5M3P

2M1P1

times119899-M

3M4-M2P

1times119899-M

4Null

Null

M1P1times

119899M1G

0times119899-M

3M1P0times119899

14circle

12Successio

nBlack

Well-

distr

ibuted

6M3P

2M4P

1times119899-M

3M4-M2P

1times119899-M

4M3P

1M4P

2M4P

2M3P

1M2P

1times119899

M1G

0times119899-M

3M1P0times119899

14circle

8Com

binatio

nYellow

Non

-well-

distr

ibuted

7M3P

2M2P

1times119899-M

3M4-M2P

1times119899-M

4Null

Null

M1P1times

119899M1G

0times119899-M

3M1P0times119899

14circle

12Successio

nYello

wWell-

distr

ibuted

8M3P

5M3P

1times119899-M

3M4-M2P

1times119899-M

4M3P

1M4P

2M4P

2M3P

1M2P

1times119899

M4-M3P

1-M3

M3P

1times119899

14circle

12Com

binatio

nYello

wWell-

distr

ibuted

9M3P

2M2P

1times119899-M

3M3-M1P0-

M3

Null

Null

M1P1times

119899M1G

0times119899-M

3M3P

1times119899

14circle

12Successio

nBlack

Well-

distr

ibuted

10M2P

0M1G

0times119899-M

3M3-M1P0-

M3

Null

Null

M2P

1times119899

M4-M3P

1-M3

M3P

1times119899

24circle

11Successio

nBlack

Well-

distr

ibuted

11M2P

0M1G

0times119899-M

3M3-M1P0-

M3

Null

Null

M2P

1times119899

M1G

0times119899-M

3M3P

1times119899

24circle

12Successio

nBlack

Well-

distr

ibuted

12M3P

2M1G

0times119899-M

3

M3-M1P0-

M4-M2P

1-M4

Null

Null

M1P1times

119899M1G

0times119899-M

3M1P0times119899

24circle

6Com

binatio

nBlack

Non

-well-

distr

ibuted

13M2P

0M1G

0times119899-M

3M3-M1P0-

M3

Null

Null

M2P

1times119899

M1G

0times119899-M

3M3P

1times119899

24circle

12Successio

nBlack

Well-

distr

ibuted

14M4P

2M4P

1times119899-M

3M3-M1P0-

M3

M3P

1M4P

2M4P

2M3P

1M2P

1-M1P1

times119899

M2P

1times119899-M

3M3P

1times119899

24circle

16Com

binatio

nYello

wNon

-well-

distr

ibuted

15M3P

2M1G

0times119899-M

3M4-M2P

1times119899-M

4Null

Null

M2P

1times119899

M1G

0times119899-M

3M3P

1times119899

24circle

12Successio

nBlack

Well-

distr

ibuted

16M3P

5M3

times119899-M

3M4-M2P

1times119899-M

4M3P

1M4P

2M4P

2M3P

1M1P1times

119899M3times119899-M

3M1P0times119899

24circle

6Com

binatio

nBlack

Non

-well-

distr

ibuted

17M4M

3P2

M3

times119899-M

3M4-M2P

1times119899-M

4Null

Null

M1P1times

119899M3times119899-M

3M1P0times119899

34circle

6Com

binatio

nBlack

Non

-well-

distr

ibuted

18M3P

5M1G

0times119899-M

3M4-M2P

1times119899-M

4Null

Null

M2P

1-M1P1

times119899

M3times119899-M

3M1P0times119899

34circle

6Com

binatio

nBlack

Non

-well-

distr

ibuted




156

105

0

2

4

6

8

10

12

14

16

18





1190

313

0

200

400

600

800

1000

1200

1400


















8 Conclusion








Competing Interests


Acknowledgments



Table6Structured

representatio

nof

thec

ompleted

OEK

Prob

lem

scenario

Con

tributors

Con

tent

Intent

Creditability

Prob

lem

IDProb

lem

name

Scenario

IDScenario

characteris

ticScenario

attribute

Experts

IDCr

itical

dimensio

n

Motion

combinatio

ndescrip

tion

Intent

descrip

tion

001

Theq

ualityof

weld

ingisno

tconstant

001

Theh

eadof

innerv

essel

Bend

ing

weld

ingargon

arcw

elding

001-L

KStep3

M3-M1P0-M4

-M2P

1-M4

Obtaintheh

igh

quality

weld

s100

002-YK

B003-CS

FStep3

M4-M2P

1times119899-M

4100

001-L

K002-YK

B003-CS

FStep5

M4P

2M3P

1Re

duce

the

pauseinthe

weld

ingprocess

100

001-L

K002-YK

B003-CS

FStep6

M2P

1times119899

Easy

toop

erate

andcontrol

strength

100

001-L

K002-YK

B003-CS

FArcingpo

int

14circle

Obtaintheh

igh

quality

weld

s80



Evaluation index

Values

Methods

Factor Attribute


and Winnips[7]


[15]


Elicitor





Informant




Problemdomain



Elicitationprocess




References



























































Volume 2014




Journal of











Journal of


Function Spaces






Algebra










M4

Position

Inspecting

Put

Start

Agent A

Grasp


M3

Processed objects

M3

No 1 pipe

M3

ldquoGoa

lrdquoPi

pes w

eldin

g

Num

ber 1

pip

e weld

ing

Weld

ing

prep

arat

ion

Weld

ing

Posit

ion

Weld

ing

mat

eria

ls pr

epar

atio

n

Subg

oal 2

Su

bgoa

l 1

Subg

oal 1

P2

M1

G0

G1

E2

P5

M3

P1

P1

M3

M1

G1

P2

M1

P1

P1

ldquoSub

goalrdquo

Qua

lity

insp

ectio

n

ldquoSub

goalrdquo

Fiel

d in

spec

tion

Subg

oal 1

Welding

M1

G0

D3

G1

M3

D3

M3

M3

D3

M3

E2

ldquoSce

nario

fact

orsrdquo

M

an-m

achi

ne in

tera

ctio

n

Hum

an-a

rtifa

cts

inte

ract

ion

ldquoSub

goalrdquo

Hum

an-to

ols

inte

ract

ion

The n

umbe

r of

rota

tions

Arc

ing

poin

t

ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo

Ges

ture

of h

oldi

ng

tool

Subg

oal 2

Su

bgoa

l 1

Subg

oal 1

Mod

e cho

osin

g

Subg

oal 3

D3 D3 D3

6 12 12


ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo





M4

Position

Inspecting

Put

Start

Agent A

Grasp


M3

ProcessedobjectsM3

Number 1

M3

ldquoGoa

lrdquoPi

pes w

eldin

g

ldquoSub

goalrdquo

Num

ber 1

pip

e weld

ing

ldquoSub

goalrdquo

Weld

ing

prep

arat

ion

ldquoSub

goalrdquo

Weld

ing

ldquoSub

goalrdquo

Posit

ion

ldquoSub

goalrdquo

Weld

ing

mat

eria

lspr

epar

atio

n

Subg

oal 2

Subg

oal 1

Subg

oal 1

P2

M1G0

G1

E2

P5

M3

P1

P1

M3

M1

G1

P2

M1

P1

P1

ldquoSub

goalrdquo

Qua

lity

insp

ectio

n

ldquoSub

goalrdquo

Fiel

d in

spec

tion

Subg

oal 1

WeldingM1G0

D3

G1

M3

D3

M3

M3

D3

M3

E2

ldquoSce

nario

fact

orsrdquo

Man

-mac

hine

inte

ract

ion

Hum

an-a

rtifa

cts

inte

ract

ion

Hum

an-to

ols

inte

ract

ion

The n

umbe

r of

rota

tions

Arc

ing

poin

tG

estu

re o

fho

ldin

g to

ol

Subg

oal 2

Subg

oal 1

Subg

oal 1

Mod

e cho

osin

g

Subg

oal 3

ldquoQua

lity

fact

orsrdquo

The q

ualit

y of

weld

ing

The s

urfa

ce q

ualit

yof

weld

ing

The c

olor

of

weld

ing

D3 D3 D3

6 12 12


Black Black Black


Well-distributed

Well-distributed

ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo



pipe



Table3Th

eresulto

fpreprocessin

g

IDStep1

Step2

Step3

Step4

Step5

Step6

Step7

Righth

and

Arcingpo

int

Then

umber

ofrotatio

nsMod

echoo

sing

Color

Surfa

cequ

ality

1M4P

2M3P

1times119899-M

3

M3-M1P0-

M4-M2P

1-M4

M3P

1M4P

2M4P

2M3P

1M2P

1times119899

M3P

1times119899-M

3M1P0times119899

14circle

8Com

binatio

nYellow

Well-

distr

ibuted

2M3P

2M1G

0times119899-M

3M4-M2P

1times119899-M

4Null

Null

M1P1times

119899M1G

0times119899-M

3M3P

1times119899

24circle

12Successio

nBlack

Well-

distr

ibuted

3M3P

2M1P1

times119899-M

3M4-M2P

1times119899-M

4Null

Null

M1P1times

119899M1G

0times119899-M

3M1P0times119899

24circle

12Successio

nBlack

Well-

distr

ibuted

4M3P

2M3P

1times119899-M

3M4-M2P

1times119899-M

4Null

Null

M2P

1times119899

M3P

1times119899-M

3M1P0times119899

14circle

12Com

binatio

nYello

wWell-

distr

ibuted

5M3P

2M1P1

times119899-M

3M4-M2P

1times119899-M

4Null

Null

M1P1times

119899M1G

0times119899-M

3M1P0times119899

14circle

12Successio

nBlack

Well-

distr

ibuted

6M3P

2M4P

1times119899-M

3M4-M2P

1times119899-M

4M3P

1M4P

2M4P

2M3P

1M2P

1times119899

M1G

0times119899-M

3M1P0times119899

14circle

8Com

binatio

nYellow

Non

-well-

distr

ibuted

7M3P

2M2P

1times119899-M

3M4-M2P

1times119899-M

4Null

Null

M1P1times

119899M1G

0times119899-M

3M1P0times119899

14circle

12Successio

nYello

wWell-

distr

ibuted

8M3P

5M3P

1times119899-M

3M4-M2P

1times119899-M

4M3P

1M4P

2M4P

2M3P

1M2P

1times119899

M4-M3P

1-M3

M3P

1times119899

14circle

12Com

binatio

nYello

wWell-

distr

ibuted

9M3P

2M2P

1times119899-M

3M3-M1P0-

M3

Null

Null

M1P1times

119899M1G

0times119899-M

3M3P

1times119899

14circle

12Successio

nBlack

Well-

distr

ibuted

10M2P

0M1G

0times119899-M

3M3-M1P0-

M3

Null

Null

M2P

1times119899

M4-M3P

1-M3

M3P

1times119899

24circle

11Successio

nBlack

Well-

distr

ibuted

11M2P

0M1G

0times119899-M

3M3-M1P0-

M3

Null

Null

M2P

1times119899

M1G

0times119899-M

3M3P

1times119899

24circle

12Successio

nBlack

Well-

distr

ibuted

12M3P

2M1G

0times119899-M

3

M3-M1P0-

M4-M2P

1-M4

Null

Null

M1P1times

119899M1G

0times119899-M

3M1P0times119899

24circle

6Com

binatio

nBlack

Non

-well-

distr

ibuted

13M2P

0M1G

0times119899-M

3M3-M1P0-

M3

Null

Null

M2P

1times119899

M1G

0times119899-M

3M3P

1times119899

24circle

12Successio

nBlack

Well-

distr

ibuted

14M4P

2M4P

1times119899-M

3M3-M1P0-

M3

M3P

1M4P

2M4P

2M3P

1M2P

1-M1P1

times119899

M2P

1times119899-M

3M3P

1times119899

24circle

16Com

binatio

nYello

wNon

-well-

distr

ibuted

15M3P

2M1G

0times119899-M

3M4-M2P

1times119899-M

4Null

Null

M2P

1times119899

M1G

0times119899-M

3M3P

1times119899

24circle

12Successio

nBlack

Well-

distr

ibuted

16M3P

5M3

times119899-M

3M4-M2P

1times119899-M

4M3P

1M4P

2M4P

2M3P

1M1P1times

119899M3times119899-M

3M1P0times119899

24circle

6Com

binatio

nBlack

Non

-well-

distr

ibuted

17M4M

3P2

M3

times119899-M

3M4-M2P

1times119899-M

4Null

Null

M1P1times

119899M3times119899-M

3M1P0times119899

34circle

6Com

binatio

nBlack

Non

-well-

distr

ibuted

18M3P

5M1G

0times119899-M

3M4-M2P

1times119899-M

4Null

Null

M2P

1-M1P1

times119899

M3times119899-M

3M1P0times119899

34circle

6Com

binatio

nBlack

Non

-well-

distr

ibuted




156

105

0

2

4

6

8

10

12

14

16

18





1190

313

0

200

400

600

800

1000

1200

1400


















8 Conclusion








Competing Interests


Acknowledgments



Table6Structured

representatio

nof

thec

ompleted

OEK

Prob

lem

scenario

Con

tributors

Con

tent

Intent

Creditability

Prob

lem

IDProb

lem

name

Scenario

IDScenario

characteris

ticScenario

attribute

Experts

IDCr

itical

dimensio

n

Motion

combinatio

ndescrip

tion

Intent

descrip

tion

001

Theq

ualityof

weld

ingisno

tconstant

001

Theh

eadof

innerv

essel

Bend

ing

weld

ingargon

arcw

elding

001-L

KStep3

M3-M1P0-M4

-M2P

1-M4

Obtaintheh

igh

quality

weld

s100

002-YK

B003-CS

FStep3

M4-M2P

1times119899-M

4100

001-L

K002-YK

B003-CS

FStep5

M4P

2M3P

1Re

duce

the

pauseinthe

weld

ingprocess

100

001-L

K002-YK

B003-CS

FStep6

M2P

1times119899

Easy

toop

erate

andcontrol

strength

100

001-L

K002-YK

B003-CS

FArcingpo

int

14circle

Obtaintheh

igh

quality

weld

s80



Evaluation index

Values

Methods

Factor Attribute


and Winnips[7]


[15]


Elicitor





Informant




Problemdomain



Elicitationprocess




References



























































Volume 2014




Journal of











Journal of


Function Spaces






Algebra










M4

Position

Inspecting

Put

Start

Agent A

Grasp


M3

ProcessedobjectsM3

Number 1

M3

ldquoGoa

lrdquoPi

pes w

eldin

g

ldquoSub

goalrdquo

Num

ber 1

pip

e weld

ing

ldquoSub

goalrdquo

Weld

ing

prep

arat

ion

ldquoSub

goalrdquo

Weld

ing

ldquoSub

goalrdquo

Posit

ion

ldquoSub

goalrdquo

Weld

ing

mat

eria

lspr

epar

atio

n

Subg

oal 2

Subg

oal 1

Subg

oal 1

P2

M1G0

G1

E2

P5

M3

P1

P1

M3

M1

G1

P2

M1

P1

P1

ldquoSub

goalrdquo

Qua

lity

insp

ectio

n

ldquoSub

goalrdquo

Fiel

d in

spec

tion

Subg

oal 1

WeldingM1G0

D3

G1

M3

D3

M3

M3

D3

M3

E2

ldquoSce

nario

fact

orsrdquo

Man

-mac

hine

inte

ract

ion

Hum

an-a

rtifa

cts

inte

ract

ion

Hum

an-to

ols

inte

ract

ion

The n

umbe

r of

rota

tions

Arc

ing

poin

tG

estu

re o

fho

ldin

g to

ol

Subg

oal 2

Subg

oal 1

Subg

oal 1

Mod

e cho

osin

g

Subg

oal 3

ldquoQua

lity

fact

orsrdquo

The q

ualit

y of

weld

ing

The s

urfa

ce q

ualit

yof

weld

ing

The c

olor

of

weld

ing

D3 D3 D3

6 12 12


Black Black Black


Well-distributed

Well-distributed

ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo

ldquoSub

goalrdquo



pipe



Table3Th

eresulto

fpreprocessin

g

IDStep1

Step2

Step3

Step4

Step5

Step6

Step7

Righth

and

Arcingpo

int

Then

umber

ofrotatio

nsMod

echoo

sing

Color

Surfa

cequ

ality

1M4P

2M3P

1times119899-M

3

M3-M1P0-

M4-M2P

1-M4

M3P

1M4P

2M4P

2M3P

1M2P

1times119899

M3P

1times119899-M

3M1P0times119899

14circle

8Com

binatio

nYellow

Well-

distr

ibuted

2M3P

2M1G

0times119899-M

3M4-M2P

1times119899-M

4Null

Null

M1P1times

119899M1G

0times119899-M

3M3P

1times119899

24circle

12Successio

nBlack

Well-

distr

ibuted

3M3P

2M1P1

times119899-M

3M4-M2P

1times119899-M

4Null

Null

M1P1times

119899M1G

0times119899-M

3M1P0times119899

24circle

12Successio

nBlack

Well-

distr

ibuted

4M3P

2M3P

1times119899-M

3M4-M2P

1times119899-M

4Null

Null

M2P

1times119899

M3P

1times119899-M

3M1P0times119899

14circle

12Com

binatio

nYello

wWell-

distr

ibuted

5M3P

2M1P1

times119899-M

3M4-M2P

1times119899-M

4Null

Null

M1P1times

119899M1G

0times119899-M

3M1P0times119899

14circle

12Successio

nBlack

Well-

distr

ibuted

6M3P

2M4P

1times119899-M

3M4-M2P

1times119899-M

4M3P

1M4P

2M4P

2M3P

1M2P

1times119899

M1G

0times119899-M

3M1P0times119899

14circle

8Com

binatio

nYellow

Non

-well-

distr

ibuted

7M3P

2M2P

1times119899-M

3M4-M2P

1times119899-M

4Null

Null

M1P1times

119899M1G

0times119899-M

3M1P0times119899

14circle

12Successio

nYello

wWell-

distr

ibuted

8M3P

5M3P

1times119899-M

3M4-M2P

1times119899-M

4M3P

1M4P

2M4P

2M3P

1M2P

1times119899

M4-M3P

1-M3

M3P

1times119899

14circle

12Com

binatio

nYello

wWell-

distr

ibuted

9M3P

2M2P

1times119899-M

3M3-M1P0-

M3

Null

Null

M1P1times

119899M1G

0times119899-M

3M3P

1times119899

14circle

12Successio

nBlack

Well-

distr

ibuted

10M2P

0M1G

0times119899-M

3M3-M1P0-

M3

Null

Null

M2P

1times119899

M4-M3P

1-M3

M3P

1times119899

24circle

11Successio

nBlack

Well-

distr

ibuted

11M2P

0M1G

0times119899-M

3M3-M1P0-

M3

Null

Null

M2P

1times119899

M1G

0times119899-M

3M3P

1times119899

24circle

12Successio

nBlack

Well-

distr

ibuted

12M3P

2M1G

0times119899-M

3

M3-M1P0-

M4-M2P

1-M4

Null

Null

M1P1times

119899M1G

0times119899-M

3M1P0times119899

24circle

6Com

binatio

nBlack

Non

-well-

distr

ibuted

13M2P

0M1G

0times119899-M

3M3-M1P0-

M3

Null

Null

M2P

1times119899

M1G

0times119899-M

3M3P

1times119899

24circle

12Successio

nBlack

Well-

distr

ibuted

14M4P

2M4P

1times119899-M

3M3-M1P0-

M3

M3P

1M4P

2M4P

2M3P

1M2P

1-M1P1

times119899

M2P

1times119899-M

3M3P

1times119899

24circle

16Com

binatio

nYello

wNon

-well-

distr

ibuted

15M3P

2M1G

0times119899-M

3M4-M2P

1times119899-M

4Null

Null

M2P

1times119899

M1G

0times119899-M

3M3P

1times119899

24circle

12Successio

nBlack

Well-

distr

ibuted

16M3P

5M3

times119899-M

3M4-M2P

1times119899-M

4M3P

1M4P

2M4P

2M3P

1M1P1times

119899M3times119899-M

3M1P0times119899

24circle

6Com

binatio

nBlack

Non

-well-

distr

ibuted

17M4M

3P2

M3

times119899-M

3M4-M2P

1times119899-M

4Null

Null

M1P1times

119899M3times119899-M

3M1P0times119899

34circle

6Com

binatio

nBlack

Non

-well-

distr

ibuted

18M3P

5M1G

0times119899-M

3M4-M2P

1times119899-M

4Null

Null

M2P

1-M1P1

times119899

M3times119899-M

3M1P0times119899

34circle

6Com

binatio

nBlack

Non

-well-

distr

ibuted




156

105

0

2

4

6

8

10

12

14

16

18





1190

313

0

200

400

600

800

1000

1200

1400


















8 Conclusion








Competing Interests


Acknowledgments



Table6Structured

representatio

nof

thec

ompleted

OEK

Prob

lem

scenario

Con

tributors

Con

tent

Intent

Creditability

Prob

lem

IDProb

lem

name

Scenario

IDScenario

characteris

ticScenario

attribute

Experts

IDCr

itical

dimensio

n

Motion

combinatio

ndescrip

tion

Intent

descrip

tion

001

Theq

ualityof

weld

ingisno

tconstant

001

Theh

eadof

innerv

essel

Bend

ing

weld

ingargon

arcw

elding

001-L

KStep3

M3-M1P0-M4

-M2P

1-M4

Obtaintheh

igh

quality

weld

s100

002-YK

B003-CS

FStep3

M4-M2P

1times119899-M

4100

001-L

K002-YK

B003-CS

FStep5

M4P

2M3P

1Re

duce

the

pauseinthe

weld

ingprocess

100

001-L

K002-YK

B003-CS

FStep6

M2P

1times119899

Easy

toop

erate

andcontrol

strength

100

001-L

K002-YK

B003-CS

FArcingpo

int

14circle

Obtaintheh

igh

quality

weld

s80



Evaluation index

Values

Methods

Factor Attribute


and Winnips[7]


[15]


Elicitor





Informant




Problemdomain



Elicitationprocess




References



























































Volume 2014




Journal of











Journal of


Function Spaces






Algebra










Table3Th

eresulto

fpreprocessin

g

IDStep1

Step2

Step3

Step4

Step5

Step6

Step7

Righth

and

Arcingpo

int

Then

umber

ofrotatio

nsMod

echoo

sing

Color

Surfa

cequ

ality

1M4P

2M3P

1times119899-M

3

M3-M1P0-

M4-M2P

1-M4

M3P

1M4P

2M4P

2M3P

1M2P

1times119899

M3P

1times119899-M

3M1P0times119899

14circle

8Com

binatio

nYellow

Well-

distr

ibuted

2M3P

2M1G

0times119899-M

3M4-M2P

1times119899-M

4Null

Null

M1P1times

119899M1G

0times119899-M

3M3P

1times119899

24circle

12Successio

nBlack

Well-

distr

ibuted

3M3P

2M1P1

times119899-M

3M4-M2P

1times119899-M

4Null

Null

M1P1times

119899M1G

0times119899-M

3M1P0times119899

24circle

12Successio

nBlack

Well-

distr

ibuted

4M3P

2M3P

1times119899-M

3M4-M2P

1times119899-M

4Null

Null

M2P

1times119899

M3P

1times119899-M

3M1P0times119899

14circle

12Com

binatio

nYello

wWell-

distr

ibuted

5M3P

2M1P1

times119899-M

3M4-M2P

1times119899-M

4Null

Null

M1P1times

119899M1G

0times119899-M

3M1P0times119899

14circle

12Successio

nBlack

Well-

distr

ibuted

6M3P

2M4P

1times119899-M

3M4-M2P

1times119899-M

4M3P

1M4P

2M4P

2M3P

1M2P

1times119899

M1G

0times119899-M

3M1P0times119899

14circle

8Com

binatio

nYellow

Non

-well-

distr

ibuted

7M3P

2M2P

1times119899-M

3M4-M2P

1times119899-M

4Null

Null

M1P1times

119899M1G

0times119899-M

3M1P0times119899

14circle

12Successio

nYello

wWell-

distr

ibuted

8M3P

5M3P

1times119899-M

3M4-M2P

1times119899-M

4M3P

1M4P

2M4P

2M3P

1M2P

1times119899

M4-M3P

1-M3

M3P

1times119899

14circle

12Com

binatio

nYello

wWell-

distr

ibuted

9M3P

2M2P

1times119899-M

3M3-M1P0-

M3

Null

Null

M1P1times

119899M1G

0times119899-M

3M3P

1times119899

14circle

12Successio

nBlack

Well-

distr

ibuted

10M2P

0M1G

0times119899-M

3M3-M1P0-

M3

Null

Null

M2P

1times119899

M4-M3P

1-M3

M3P

1times119899

24circle

11Successio

nBlack

Well-

distr

ibuted

11M2P

0M1G

0times119899-M

3M3-M1P0-

M3

Null

Null

M2P

1times119899

M1G

0times119899-M

3M3P

1times119899

24circle

12Successio

nBlack

Well-

distr

ibuted

12M3P

2M1G

0times119899-M

3

M3-M1P0-

M4-M2P

1-M4

Null

Null

M1P1times

119899M1G

0times119899-M

3M1P0times119899

24circle

6Com

binatio

nBlack

Non

-well-

distr

ibuted

13M2P

0M1G

0times119899-M

3M3-M1P0-

M3

Null

Null

M2P

1times119899

M1G

0times119899-M

3M3P

1times119899

24circle

12Successio

nBlack

Well-

distr

ibuted

14M4P

2M4P

1times119899-M

3M3-M1P0-

M3

M3P

1M4P

2M4P

2M3P

1M2P

1-M1P1

times119899

M2P

1times119899-M

3M3P

1times119899

24circle

16Com

binatio

nYello

wNon

-well-

distr

ibuted

15M3P

2M1G

0times119899-M

3M4-M2P

1times119899-M

4Null

Null

M2P

1times119899

M1G

0times119899-M

3M3P

1times119899

24circle

12Successio

nBlack

Well-

distr

ibuted

16M3P

5M3

times119899-M

3M4-M2P

1times119899-M

4M3P

1M4P

2M4P

2M3P

1M1P1times

119899M3times119899-M

3M1P0times119899

24circle

6Com

binatio

nBlack

Non

-well-

distr

ibuted

17M4M

3P2

M3

times119899-M

3M4-M2P

1times119899-M

4Null

Null

M1P1times

119899M3times119899-M

3M1P0times119899

34circle

6Com

binatio

nBlack

Non

-well-

distr

ibuted

18M3P

5M1G

0times119899-M

3M4-M2P

1times119899-M

4Null

Null

M2P

1-M1P1

times119899

M3times119899-M

3M1P0times119899

34circle

6Com

binatio

nBlack

Non

-well-

distr

ibuted




156

105

0

2

4

6

8

10

12

14

16

18





1190

313

0

200

400

600

800

1000

1200

1400


















8 Conclusion








Competing Interests


Acknowledgments



Table6Structured

representatio

nof

thec

ompleted

OEK

Prob

lem

scenario

Con

tributors

Con

tent

Intent

Creditability

Prob

lem

IDProb

lem

name

Scenario

IDScenario

characteris

ticScenario

attribute

Experts

IDCr

itical

dimensio

n

Motion

combinatio

ndescrip

tion

Intent

descrip

tion

001

Theq

ualityof

weld

ingisno

tconstant

001

Theh

eadof

innerv

essel

Bend

ing

weld

ingargon

arcw

elding

001-L

KStep3

M3-M1P0-M4

-M2P

1-M4

Obtaintheh

igh

quality

weld

s100

002-YK

B003-CS

FStep3

M4-M2P

1times119899-M

4100

001-L

K002-YK

B003-CS

FStep5

M4P

2M3P

1Re

duce

the

pauseinthe

weld

ingprocess

100

001-L

K002-YK

B003-CS

FStep6

M2P

1times119899

Easy

toop

erate

andcontrol

strength

100

001-L

K002-YK

B003-CS

FArcingpo

int

14circle

Obtaintheh

igh

quality

weld

s80



Evaluation index

Values

Methods

Factor Attribute


and Winnips[7]


[15]


Elicitor





Informant




Problemdomain



Elicitationprocess




References



























































Volume 2014




Journal of











Journal of


Function Spaces






Algebra












156

105

0

2

4

6

8

10

12

14

16

18





1190

313

0

200

400

600

800

1000

1200

1400


















8 Conclusion








Competing Interests


Acknowledgments



Table6Structured

representatio

nof

thec

ompleted

OEK

Prob

lem

scenario

Con

tributors

Con

tent

Intent

Creditability

Prob

lem

IDProb

lem

name

Scenario

IDScenario

characteris

ticScenario

attribute

Experts

IDCr

itical

dimensio

n

Motion

combinatio

ndescrip

tion

Intent

descrip

tion

001

Theq

ualityof

weld

ingisno

tconstant

001

Theh

eadof

innerv

essel

Bend

ing

weld

ingargon

arcw

elding

001-L

KStep3

M3-M1P0-M4

-M2P

1-M4

Obtaintheh

igh

quality

weld

s100

002-YK

B003-CS

FStep3

M4-M2P

1times119899-M

4100

001-L

K002-YK

B003-CS

FStep5

M4P

2M3P

1Re

duce

the

pauseinthe

weld

ingprocess

100

001-L

K002-YK

B003-CS

FStep6

M2P

1times119899

Easy

toop

erate

andcontrol

strength

100

001-L

K002-YK

B003-CS

FArcingpo

int

14circle

Obtaintheh

igh

quality

weld

s80



Evaluation index

Values

Methods

Factor Attribute


and Winnips[7]


[15]


Elicitor





Informant




Problemdomain



Elicitationprocess




References



























































Volume 2014




Journal of











Journal of


Function Spaces






Algebra
















8 Conclusion








Competing Interests


Acknowledgments



Table6Structured

representatio

nof

thec

ompleted

OEK

Prob

lem

scenario

Con

tributors

Con

tent

Intent

Creditability

Prob

lem

IDProb

lem

name

Scenario

IDScenario

characteris

ticScenario

attribute

Experts

IDCr

itical

dimensio

n

Motion

combinatio

ndescrip

tion

Intent

descrip

tion

001

Theq

ualityof

weld

ingisno

tconstant

001

Theh

eadof

innerv

essel

Bend

ing

weld

ingargon

arcw

elding

001-L

KStep3

M3-M1P0-M4

-M2P

1-M4

Obtaintheh

igh

quality

weld

s100

002-YK

B003-CS

FStep3

M4-M2P

1times119899-M

4100

001-L

K002-YK

B003-CS

FStep5

M4P

2M3P

1Re

duce

the

pauseinthe

weld

ingprocess

100

001-L

K002-YK

B003-CS

FStep6

M2P

1times119899

Easy

toop

erate

andcontrol

strength

100

001-L

K002-YK

B003-CS

FArcingpo

int

14circle

Obtaintheh

igh

quality

weld

s80



Evaluation index

Values

Methods

Factor Attribute


and Winnips[7]


[15]


Elicitor





Informant




Problemdomain



Elicitationprocess




References



























































Volume 2014




Journal of











Journal of


Function Spaces






Algebra










Table6Structured

representatio

nof

thec

ompleted

OEK

Prob

lem

scenario

Con

tributors

Con

tent

Intent

Creditability

Prob

lem

IDProb

lem

name

Scenario

IDScenario

characteris

ticScenario

attribute

Experts

IDCr

itical

dimensio

n

Motion

combinatio

ndescrip

tion

Intent

descrip

tion

001

Theq

ualityof

weld

ingisno

tconstant

001

Theh

eadof

innerv

essel

Bend

ing

weld

ingargon

arcw

elding

001-L

KStep3

M3-M1P0-M4

-M2P

1-M4

Obtaintheh

igh

quality

weld

s100

002-YK

B003-CS

FStep3

M4-M2P

1times119899-M

4100

001-L

K002-YK

B003-CS

FStep5

M4P

2M3P

1Re

duce

the

pauseinthe

weld

ingprocess

100

001-L

K002-YK

B003-CS

FStep6

M2P

1times119899

Easy

toop

erate

andcontrol

strength

100

001-L

K002-YK

B003-CS

FArcingpo

int

14circle

Obtaintheh

igh

quality

weld

s80



Evaluation index

Values

Methods

Factor Attribute


and Winnips[7]


[15]


Elicitor





Informant




Problemdomain



Elicitationprocess




References



























































Volume 2014




Journal of











Journal of


Function Spaces






Algebra











Evaluation index

Values

Methods

Factor Attribute


and Winnips[7]


[15]


Elicitor





Informant




Problemdomain



Elicitationprocess




References



























































Volume 2014




Journal of











Journal of


Function Spaces






Algebra




















































Volume 2014




Journal of











Journal of


Function Spaces






Algebra









research article a novel method for acquiring engineering...

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