an ontology and ahp based quality evaluation approach for

Research ArticleAn Ontology and AHP Based Quality Evaluation Approach forReuse Parts of End-of-Life Construction Machinery

Fei-xiang Xu Xin-hui Liu Wei Chen Chen Zhou and Bing-wei Cao

School of Mechanical and Aerospace Engineering Jilin University Changchun 130022 China

Correspondence should be addressed to Wei Chen chenwei 1979jlueducn

Received 6 June 2018 Accepted 16 August 2018 Published 13 September 2018

Academic Editor Enrique Onieva

Copyright copy 2018 Fei-xiang Xu et al This is an open access article distributed under the Creative Commons Attribution Licensewhich permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited

Effective reuse and recycling of end-of-life (EOL) products are essential for improving resource efficiency and protectingenvironment Currently many parts of construction machinery in EOL stage can still be reused directly because they are designedwith extreme high strength to meet the bad working conditions which minimizes the impact on the environment In this contexta quality evaluation approach for reuse parts is proposed in this paper Ontology model is built for representing evaluationinformationwith semantic properties and constructing the semantic relevance among the various concepts involved in parts reusingdomain thus achieving the integrating sharing and reusing of evaluating parts knowledgeOn that basis analytic hierarchy process(AHP) is put forward to quantify the reusability degree of the parts of EOL construction machinery Furthermore combined withontology rule-based-reasoning method is utilized to get suggested strategies for reuse parts In addition a web-based system isdeveloped to assist manufacturers in managing reuse parts and a case study is analyzed to demonstrate the proposed qualityevaluation approach

1 Introduction

With growing awareness for environmental protection andeconomic benefits sustainable EOL products managementhas become a vital issue around the world especially invehicles industry many researches have been conductednot only to improve parts recyclability but also to reducetheir damage of original value [1ndash5] For example ShameemAhmed et al [1] proposed a decision-making method inselecting the best compromise EOL vehicles managementalternative (eg reuse remanufacturing and recycling) withrespect to the sustainable criteria based on decision-makingtrial and evaluation laboratory and fuzzy AHP methodMeanwhile due to the economic benefits and the environ-mental consciousness more and more countries have beendevoting themselves to establish legislations that enforcedthe equipment manufacturers to accept responsibility of thecomplete life cycle of vehicle and urged firms to disassemblerecycle and reuse components of EOL vehiclesThe EuropeanUnion forced the respective countries to achieve the recyclingtarget of 85 and a total recovery of 95 by 2015 [6]

Construction machinery is a kind of engineering vehiclewhose parts in EOL stage have several important recoverystrategies with little environmental damage and energy wastesuch as reuse remanufacturing recycling etc PengxingYi et al [7] focused on the optimal design of a retaileroriented closed-loop supply chain network for the EOLconstruction machinery remanufacturing based on geneticalgorithm Aiming at maximizing resource utilization andproduce profits Haolan Liao et al [8] applied an optimizingmathematical analysis to the EOL engineering machineryrecovering in a joint manufacturing system

Compared with other vehicles components of construc-tionmachinery are designed andmanufactured with extremehigh strength to meet the bad working conditions In thiscontext many parts of construction machinery in EOL stageare able to still be reused directly as new parts or be repairedwith less manufacturing process Hence direct reuse of partsin the EOL construction machinery has great economicprofits and is worth our research

In order to realize direct reuse of parts in the EOL con-struction machinery a specific quality evaluation approach

HindawiMathematical Problems in EngineeringVolume 2018 Article ID 3481030 12 pageshttpsdoiorg10115520183481030

2 Mathematical Problems in Engineering

needs to be proposed to quantify the reusability degree ofthe parts which determines whether the components canbe reused directly Many researches have been conductedon evaluation approach in construction machinery domainand other industries Jun Zhou et al [9] presented a qual-ity evaluation model to quantify the reusability degree ofthe recycling parts on the EOL wheel loader (a kind ofconstruction machinery) based on the fuzzy AHP Tsai ChiKuo et al [10] put forward a recyclability evaluation methodbased on case-based reasoning and AHP Che-Wei Tsui etal [11] proposed a hybrid multiple criteria group decision-making method based on AHP to assist the manufacturerin choosing four polarizer suppliers Kuo Chang et al[12] proposed a green fuzzy design analysis approach thatcomprises simple and efficient procedures to evaluate productdesign alternatives based on environmental considerationand AHP Shahed Shojaeipour [13] evaluated a sustainablemanufacturing process planning schema through identifyingand quantifying the environmental impacts based on AHPYing Xiang et al [14] proposed the improved fuzzy AHPmethod to analyze and evaluate brittle source of the keyprocedure in increasing the stability during complex partsrsquomanufacturing

Inspired by previouswork AHP is applied into the qualityevaluation approach in this paper however this method hasdrawbacks as follows

(1) It is difficult to propose a universal quality evaluationmodel for reuse parts of all EOL construction machineriesbecause the types of construction machineries are diversesuch as wheel loader crawler excavator bulldozers etcwhich leads to many quality evaluation methods for differentreuse components in various construction machineries

(2) It is hard to reuse the existing quality evaluationmodelof reuse parts Numerous scholars have to reestablish modelsand methods during the study of the quality evaluationresulting in waste of research energy

Ontology is the solution for two defects proposed abovewhich is a mechanism that describes concepts and theirsystem relationships [15 16] On the one hand for the highlyexpansibility of ontology model the classes properties andindividuals in the ontologymodel can be continually updatedand enhanced [17 18] The quality evaluation for reuseparts of different engineering machineries can be realized bychanging the knowledge in the ontology model dynamicallythus realizing a universal quality evaluation approach Onthe other hand ontology can enable the computer to easilyunderstand and integrate the knowledge which achieves thesharing and reuse of quality evaluation knowledge [19 20]In this way existing quality evaluation model of reuse partscan be reused Hence ontology concept will be exploited toovercome two problems mentioned above in this paper

With the rapid development of knowledge technologyontology has been applied into areas like evaluation approachYu-Jun Wang et al [21] developed an ontology-based coldchain logistics monitoring and decision system The systemmakes evaluation and decision support for the monitoredcold chain quality VanessaAyala-Rivera et al [22] introduceda measurement scheme based on ontologies to quantita-tively evaluate the quality of value generalization hierarchies

aiming at producing higher-quality anonymizations Xiao-min Zhu et al [23] proposed an ontology-based knowledgemodel to evaluate water quality through using rule-based rea-soning method Chau KW et al [24] presented an ontology-based knowledge management system for water qualityevaluation Xiao-Ci Huang et al [20] proposed an ontologymodelling approach to evaluate the river water quality basedon rule-based reasoning method Edward Corry et al [25]built an ontology-based performance evaluation model forthe environmental and energy management of buildings

At present combining ontology and AHP is few studiedin the quality evaluation for reuse parts of EOL constructionmachinery and this paper is aimed to fill up this gap

The ontology model is built for representing qualityevaluation parameters of reuse parts with semantic propertiesand constructing the semantic relevance among the variousconcepts involved in quality evaluation domain On thatbasis AHP is put forward to quantify the reusability degreeof the parts of EOL construction machinery to determinewhether the components can be reused directly Furthermorerule-based reasoning method is utilized to realize decisionsupport for reuse measures In addition a web-based systemis developed to assist in managing reuse parts and a casestudy is analyzed to demonstrate the proposed quality eval-uation approach

The remainder of this paper is organized as followsSection 2 mainly focuses on quality evaluation approach forreuse parts of EOL construction machinery Section 3 is thecase study to demonstrate the proposed quality evaluationapproach Section 4 is the discussion about advantages ofproposed methods Section 5 is the conclusions

2 Methodology

This paper mainly focuses on quality evaluation approachfor reuse parts of EOL construction machinery to help usersdetermine whether the components can be reused directlybased on ontology and AHP Figure 1 shows the overallframework of quality evaluation approach

Firstly the ontologymodel is the core part of thismethodwhich is used for representing related quality evaluationinformation of reuse parts with semantic properties Sec-ondly ontology querying based on Sparql (Simple Protocoland RDF Query Language) is executed to query relatedquality evaluation information Thirdly AHP is proposedto quantify the reusability degree of the parts Then SWRLrules are constructed to reason the suggested actions abouthow to deal with reuse parts Finally a web-based system isdeveloped on the visual studio 2017 platform with programlanguage C to assist users in managing reuse parts

21 Structure of Ontology Model The ontology has highlyexpansibility and can enable the computer to easily under-stand the knowledge which makes a formalised definitionto the knowledge of parts reusing All related information ofevaluating parts is expressed and stored into ontology modelProtege [26] software builds ontology model because of itsfriendly interface powerful tools and data checking feature[27] In the Protege editor the construction of ontology

Mathematical Problems in Engineering 3

Table 1 Main classes in the ontology model

No Class Subclass Description1 Equipment Indicate the basic information of reuse parts

2 Parameters

ImportantDimensionSubordinateDimensionGeometricTolerance

NonSurfaceAppearanceDamage

PhysicalDefectAssemblyTolerance

Indicate seven judging features for quality evaluation model ofreuse parts These features are derived from reference [9] and

related testing standards of mechanical parts

3 ReuseLevel Indicate the reusability degree of the parts4 Actions Indicate suggested reuseremanufacturingrecycle strategies etc

Table 2 Object properties in the ontology model

No Data property Domains Ranges Description1 Has Part Equipment Parameters Evaluation features belong to reuse parts2 Has Cause Parameters ReuseLevel Evaluation features decide the reusability degree of the parts3 Has Level Equipment ReuseLevel Indicate partsrsquo reusability level

4 Has Action Equipment Actions Reuse recycledispose strategies should be exploited in the partsReuseLevel

Ontology querying based on Sparql

Ontology model created by Proteacutegeacute

Equipment Parameters

Reusability Actions

Ontology reasoning based on SWRL rules

AHP-based evaluation

A Web-based management system

Related quality evaluation information of reuse parts

Figure 1 Development flow of quality evaluation approach of reuseparts

model can be decomposed to the definitions of a series ofclasses object properties and data properties [28 29]

(i) Define Classes and Class Hierarchy Firstly Table 1 showsthe classes that the ontology contains These classes involveequipment (Equipment) parameters (Parameters) reusabilitydegree (ReuseLevel) and suggested reuseremanufacturingrecycle strategies (Actions) The four subclasses of classParameters are derived from [9] and other subclasses aredescribed as follows

AppearanceDamage It is usuallymeasured from those factorsincluding surface roughness thickness of coating coatingstrength coating material surface wear welding procedureand so on which has great important effect on the usage andservice life of reuse parts

Physical Defect It mainly contains material corrosioncrackle dent plaque etc Moreover the defects of surfacetreatment including fragment surface crack and un-eventexture also potentially affect the safety of reuse parts Hencethe physical defect is the utmost important evaluation feature

Assembly Tolerance Relative to geometric tolerance assemblytolerance represents the coordination accuracy of assembledholes and axes which plays a great role in the assembly ofreuse parts Although other evaluation features are fine partsare not able to be reused directly once assembly toleranceof parts does not meet the requirements Therefore theassembly tolerance is another important evaluation element

(ii) Defining Object Properties As shown in Table 2 eachobject property has its corresponding domains and ranges

(iii) Defining Data Properties As shown in Table 3 each dataproperty similar to object property also has its own domainsand ranges

After the three steps mentioned above structure of theontology model can be constructed as shown in Figure 2It can be clearly seen that the ontology is composed of theclasses the object attributes and the data attributes Amongthem ldquoclassesrdquo represent a set of individuals in the reuse partsdomain ldquoobject propertiesrdquo construct the semantic relevanceamong the various classes ldquodata propertiesrdquo provide varieddescriptions to the classes


Table 3 Data properties in the ontology model

No Data property Domains Ranges Description

1 typeScore Parameters float Indicate the importance of each evaluation featurersquostype for determining reusability degree of parts

2 deviationScore Parameters float Indicate the significance of damage degree of eachevaluation feature for deciding parts reusability degree

3 locationScoreNonSurface

floatRecord the essentiality of locations of three evaluationfeatures(non-working surface damage appearance

damage physical defect) for evaluating parts reusabilitylevel

AppearanceDamagePhysicalDefect

4 hasMaxValue ReuseLevel float Indicate maximum value of reusability degree5 hasMinValue ReuseLevel float Record minimum value of reusability degree6 madeFactory Equipment string Indicate manufacturers of reuse parts7 madeTime Equipment dataTime Record manufacturing time types of reuse parts

EquipmentmadeFactory

madeTime

Actions

ReuseLevel

Parameters

Has_Action

Has_CauseHas_Part

Has_Action

Has_Level

ImportantDimensiontypeScore

deviationScore

SubordinateDimensiontypeScore

deviationScore

GeometricTolerancetypeScore

deviationScore

NonSurfacetypeScore

deviationScorelocationScore

AppearanceDamagetypeScore


PhysicalDefecttypeScore


AssemblyTolerancetypeScore

deviationScore

Figure 2 Structure of ontology model

22 Ontology Querying Based on Sparql According to Fig-ure 1 ontology parsing is precondition of ontology queryingJena is an open source Java software for the developmentof application programs in semantic web and it containsan ontology subsystem supporting OWL DAML+OIL andRDFS Moreover Jena can parse ontology model On thatbasis JenaAPI is used to parse ontologymodel through usingldquocomhphp1jenaontologyrdquo package Using Jena API in theVisual Studio 2017 development platform mainly includesthe following three steps first Jena package is imported intoVisual Studio 2017 and the corresponding namespaces shouldbe inserted into the program secondly ontology model isinstantiated based on ldquocreateOntologyModel()rdquomode finallythe owl document describing ontology model is read With

three steps mentioned above the analysis of the ontologymodel is completed

After the achievement of ontology parsing ontologyquerying is used to obtain the related evaluation informationin the ontology model based on Sparql which is a languagethat enables the query of information in the RDFmodelThisarticle mainly takes advantage of Sparqlrsquos query function toget the related evaluation informationThe results that Sparqlquires are stored into thetxt file and then the informationis extracted from thetxt file according to demand Sparqlmainly adopts four kind of forms to query informationthrough using the pattern matching method to get the RDFgraph or result set The features of four querying forms arelisted as follows


Set up hierarchical analysis model

Construct judgment matrix

Calculate eigenvector and weight vector corresponding to the judgment matrix

Consistency check of judgment matrix

Calculate the weights of features in all levels

Obtain the scores of features in all levels judged by experts

Comprehensive evaluation

failed

successful

AHP

Figure 3 Quality evaluation process of reuse parts

CONSTRUCE the output is displayed in the form ofa RDF diagramASK whether the query results match is indicated bya BooleanDESCRIBE the information found is returnedSELECT all or part of the information that bindsvariables in querying mode is obtained

The SELECT querying form of Sparql is used to obtainthe required evaluation information in the data attributes andobject properties of the ontologymodel Next combinedwithqueried evaluation information the quality evaluation andadvisable reuse strategies are realized based onAHP and rule-based reasoning

23 Quality Evaluation Model of Parts Reusability AHPmethod is used to calculate the priority weight of each eval-uation feature After that the score of feature for evaluatingpart is adopted which is set by expertrsquos experience comparedwith the standard part Finally a comprehensive evaluationresult is achieved Quality evaluation process of reuse parts isshown in Figure 3

(1) Setting Up Hierarchical Analysis Model The hierarchicalanalysis model is shown in Figure 4 where the first levelevaluation feature is made up of seven subclasses of classParameters in the ontology model and the second levelevaluation feature includes seven subclassesrsquo data propertiestypeScore deviationScore and locationScore of the built ontol-ogy model Hence structure of hierarchical analysis model isderived from the ontology querying to acquire the priority ofevaluation feature in each level

Table 4 RI values table

n 1 2 3 4 5 6 7 8RI 0 0 058 09 112 124 132 141

(2) Constructing Judgment Matrix A The judgment matrixA is used to calculate the relative importance of evaluationfeatures in each hierarchical level A is constructed by using anominal scale from 0 to 9 with the value 119886119894119895 which is assignedto represent the judgment concerning the relative importanceof evaluation element 119886119894 over 119886119895 This important comparisonbetween evaluation features constructs the matrix 119860 = 119886119894119895(3) Calculating Eigenvector Vector Corresponding to JudgmentMatrix A The eigenvector vector (or weight vector) can beobtained by the normalization process for judgment matrixA which is described as 119882 = (1199081 1199082 119908119894)119879 where 119894denotes numbers of evaluation features in each level

(4) Consistency Check of Judgment Matrix CI is usuallyused as an indicator of consistency deviation for checkingjudgment matrix which has the following relationship

119862119868 = 120582max minus 119899119899 minus 1 (1)

where n is the order of judgmentmatrixA and120582max is the rootofmaximumeigenvector valueMeanwhile120582max is calculatedas follows

120582max =119899

sum119894=1

(119860119882)119894119899119882119894 (2)

whereA is judgematrixW is eigenvector vector n is numbersof evaluation features in each level and119882119894 is the weight of 119894thevaluation features

Furthermore CR is introduced to check consistencyefficiently and it is shown in the following formula

119862119877 = 119862119868119877119868 (3)

where CR is the ratio of consistency check RI is referencecriterion and the value of RI is shown in Table 4 If the valueof CR is less than 010 the consistency check is successful

(5) Calculating the Weight of Evaluation Features in EachLevel If consistency check of judgment matrix is satisfiedthen the obtained eigenvector vector is the weight of eval-uation features in each level otherwise eigenvector vectorhas to be calculated According to four steps mentionedabove the weights of evaluation features in two levels can becalculated If119882119891119894119903119904119905 = 119862119894119882sec119900119899119889 = 119862119895 then the weight ofthe lowest level feature is described as follows

119862119894119895 = 119862119894 lowast 119862119895 (4)

where 119862119894119895 indicates the weight of 119895th feature in second levelrelative to the weight of 119894th feature in first level

(6) Obtaining the Score of Evaluation Features in All LevelsThe scores of evaluation features in all levels are set by


Table 5 The parts reusability degree of comprehensive evaluation

Reusability degree M 0 leM lt 3 3 leM lt 5 5 leM lt 7 7 leM lt 9 9 leM le 10Reusability level I II III IV VSuggested action Discard Recycle Complex remanufacturing Simple remanufacturing Direct reuse

important dimension

subordinate dimension

geometric tolerance

non-working surface damage

appearance damage

locationScore

typeScore deviationScore


typeScore


deviationScore


Quality evaluation features of

parts reusability

physical defect

assembly tolerance

locationScore

typeScore deviationScore locationScore


First level feature Second level feature

Figure 4 Hierarchical analysis model of AHP

expertrsquos experience compared with the standard part andthey are obtained through querying data properties typeScoredeviationScore and locationScore of the built ontologymodelMoreover the experts score evaluation features according toprofessional knowledge about construction machinery andactual state of reuse parts

(7) Comprehensive Evaluation Combining AHP methodand the scores of evaluation features obtained by ontologyquerying a comprehensive evaluation is realized Supposing119878119894119895(0 le 119878119894119895 le 10) is the score of 119895th feature in secondlevel relative to the score of ith feature in first level Thecomprehensive evaluation score M is described as follows

119872 =119901

sum119894=1

119902

sum119895=1

(119862119894119895 lowast 119878119894119895) (5)

The parts reusability degree of comprehensive evaluationis shown in Table 5 The bigger value of M is the better partsreusability degree isThe threshold of parts reusability degreeM is composed of data properties hasMaxValue and hasMin-Value in the ontology model and each manufacture also candeterminemore proper threshold values ofMdefined by theirown experts to decide whether the part is worthy of reusingand recycling Reusability level corresponds to the instancesof class ReuseLevel

24 Decision Support for Recommended Strategies of ReuseParts As shown in Table 5 the recommended actionsdepend on reusability level of parts in this paper becausethe biggest goal of this paper is to inform manufacturersabout the reusability degree of the parts of EOL constructionmachinery On the basis of reusability level of parts in order


to obtain recommended actions based on the relationshipsamong Has Part Has Cause Has Action and Has LevelJena reasoner is used as tools for reasoning together withcustom rules described by SWRL (Semantic Web Rule Lan-guage) The SWRL rule is presented in the form of a pairof antecedents and consequents that is ldquoantecedents 997888rarrconsequentsrdquo Each SWRL rule means that if the conditionsspecified in the antecedents are true then the term describedin the consequents must be true [30] The antecedents andthe consequents in the SWRL rule can be composed of morethan one element and a group of elements can be written as1198861 and 1198862 and 119886119899 The variable in the SWRL rule is generally aninstance of class or a value of its data property For variablesin the SWRL rule a question mark () must be added beforeeach variable as a prefix for example ldquoxrdquo [31] Two basicelements of the SWRL rule are shown as follows [30]

(1) C (x) if variable x is an instance of class C or a valueof its data property then C (x) is valid

(2) P(x y) if variable x is connected to variable y byusing the property P then P(x y) is found

Based on the SWRL rule description mentioned aboveand the analysis of classes and properties in the ontologymodel the SWRL rules built for Jena reasoner are as follows

Rule1 Has Part(xy) and Has Cause(yz) 997888rarrHas Level( x z)Rule2 Has Level(xy) and Has Action(yz) 997888rarrHas Action( x z)Rule3 Has Part(xy) and Has Cause(yz) andHas Action(zm) 997888rarrHas Action (x m)

In order to simplify the discussion here we will onlyexplain rule1 which means that if equipment x has evaluationfeatures y and y can lead to reusability degree z thenequipment x has reusability degree z

Since related evaluation information with semantic prop-erties and the semantic relevance among the various con-cepts has been defined in ontology model the suggestedreuseremanufacturingrecycledispose strategies for reuseparts are the results of comprehensive consideration ofontology model and reasoning rules described above

25 Development of a Web-Based Management System forReuse Parts In order to obtain reusability degree resultsand manage whole processing procedures related to sug-gested reuseremanufacturingrecycledispose strategies con-veniently a web-based management system is developedbased onASPNETMVCwebsite development technology onthe visual studio 2017 platform with program language C

The web-based system is developed to provide user inter-face for users including manufactures experts and admin-istrators Manufactures can know how to deal with reuseparts of EOL construction machinery through managingsustainable EOL products Experts are responsible for scoringthe evaluation features based on their professional knowledgeand actual state of reuse parts Administrators have superauthority to manage all reuse parts owned by manufactures

Web-based system Query

management

Information management

Quality evaluation

Users management

Modify user information

Set user permissions

Add and delete users

Parts reusability degree

Recommended actions

Query basic information of reuse

parts

Manage related information about suggested strategies

Figure 5 Functional modules of web-based management system

As shown in Figure 5 the web-based system contains the fol-lowing functional modules users management informationmanagement quality evaluation and query management

(1) Users management Only administrators have theprivilege to use this functional module which containsmodify user information set user permissions add users anddelete users

(2) Information management It offers web-baseduser interface to manage related information aboutreuseremanufacturingrecycledispose strategies

(3) Quality evaluation It provides users with compre-hensive evaluation results which consist of parts reusabilitydegree and recommended actions for reuse parts

(4) Query management Users can query basic informa-tion of reuse parts such as manufacturer of reuse parts andmanufacturing time of reuse parts

3 Case Study

This section takes five parts of EOL wheel loader and crawlerexcavator as examples to verify the validation of the proposedquality evaluation approach Based on ontology andAHP thetechnical route of parts quality evaluation for case study isshown in Figure 6 Firstly based on structure of ontologymodel the reuse parts ontology model is built after instan-tiation Secondly related quality evaluation information ofreuse parts is queried based on ontology querying methodThirdly AHP is applied to calculate all weights of secondlevel evaluation features to quantify the reusability degreeof the comprehensive evaluating parts Fourthly rule-based-reasoning method is used to reason the decision support forsuggested actions about how to deal with reuse parts Fifthlya web-based system is developed to assist users in managing


Structure of ontology model Reuse_I

Reuse_II

Reuse_III

Reuse_V

typeScore

deviationScore

locationScore

AppearanceDamage

PhysicalDefect

The decision support for suggested

measures of reuse parts

Reuse_IV

Implementation of ontology model

instantiation

Caluculate all weights of second level evaluation features based on AHP

Mission

1Reusability degree

2Recommended actions

A web-basedmanagement system

Figure 6 Technical route of parts quality evaluation for case study

reuse parts of EOL constructionmachinery Finally the userscomplete the missions for achieving reusability degree ofthe parts and recommended actions (reuse parts directly orreuse parts after remanufacturing or recycle parts or disposeparts) through using the web-based system based on Internetand computer

Step 1 (implementation of ontology model) Implementationof ontology model needs to initiate classes in the structureof ontology model Five instances of ReuseLevel are createdin this paper to represent different kinds of parts reusabilitylevels and they are Reuse I Reuse II Reuse III Reuse IVand Reuse V From Reuse I to Reuse V evaluating parts havehigher reusability degree The Equipment class has instancesincluding Transition Joint Piston Rod Bracket BatteryLeft Fender and Boom Each subclass of Parameters hasfive instances corresponding to each instance of Equipmentclass The instances in class Actions are direct reusesimple remanufacturing complex remanufacturing recycleand discard So far the ontology model has been establishedand Protege software is used to build the ontology modelas shown in Figure 7 Classes instances and properties arelisted in order from left to right of Figure 7

Step 2 (comprehensive evaluation based on AHP) Then acomprehensive evaluation is realized based on AHP There is

a transition joint which is used to connect the priority valveand the hydraulic pipe of EOL wheel loader as an exampleThe hierarchical analysis model is built based on the classesand data properties of ontology model as shown in Figure 4With the scale of 1 to 9 and results of mutual comparisonabout importance of evaluation features in first hierarchicallevel judgment matrix A is constructed as follows

119860 =

[[[[[[[[[[[[[[[[[[[[[

1 5357 55459563

5 137 33413127

573 1 7

7479761

51317 11419164

54347 4 1

49239

5 397 994 1326

5 267 63223 1

]]]]]]]]]]]]]]]]]]]]]

(6)

And then the eigenvector vector of A is as follows

119882= [01429 00857 02000 00286 01143 02571 01714]119879

(7)


PropertiesClasses Instances

Figure 7 Reuse parts ontology model built in the Protege software

The root of maximum eigenvector value is as follows

120582max =119899

sum119894=1

(119860119882)119894119899119882119894 = 7 (8)

The ratio of consistency check is as follows

119862119877 = 119862119868119877119868 =120582max minus 119899(119899 minus 1) lowast 119877119868 =

7 minus 7(7 minus 1) lowast 132 = 0 lt 010 (9)

where value of CRmeans that consistency check of judgmentmatrix is satisfied thus obtaining the weights of evaluationfeatures in the first level

(01429 00857 02000 00286 01143 02571 01714) (10)

Repeating above calculation processes seven eigenvectorvectors of the second level features are obtained Accordingto (4) all the weights of 119895th feature in second level relative tothe weight of 119894th feature in first level are as follows

(00536 00893 00171 00686 00750 0125 00063 00117 00105 00336 00454 00605 00756 00454 01361 00343 01371) (11)

The scores of evaluation features in the second hierarchi-cal level are judged by professional experts according to actualstates of evaluated parts which is obtained by querying dataproperties typeScore deviationScore and locationScore of thebuilt ontology model as shown in Table 6

According to (4) the comprehensive evaluation score Mis as follows

119872 = 77492 (12)

Combined with hasMaxValue and hasMinValue of classReuseLevel in Table 5 reusability level of comprehensiveevaluating parts is in fourth grade Reuse IV

Step 3 (recommended treatments for reuse parts) Finallywith the quality evaluate result the proposed approach couldfurther go on the reasoning task with the defined SWRL rules

and the ontology model to get the recommended treatmentsfor reuse parts The result of the reasoning process fortransition joint is illustrated in Table 7 Quality evaluationresults for five reuse parts of EOL constructionmachinery aredisplayed in Figure 8

With the same steps mentioned above ontology andAHP based evaluation results for five reuse parts of EOLconstruction machinery are listed in Table 7 It can be seenthat the quality evaluation results for reuse parts of EOLconstruction machinery can be obtained through using theontology and AHP based method proposed in this paper

4 Discussion

In order to improve resource efficiency and protect environ-ment the study in this paper concentrates on the reuse parts


Table 6 Quality evaluation of a transition joint of EOL wheel loader

No All the weights of 1st level evaluationfeature

All the weights of 2nd level evaluationfeature relative to 1st level feature

Scores of 2nd level evaluation featurejudged by professional experts

1 important dimension typeScore(00536) 6(weight01429) deviationScore(00893) 9

2 subordinate dimension typeScore(00171) 8(weight00857) deviationScore(00686) 7

3 geometric tolerance typeScore(00750) 8(weight02000) deviationScore(00125) 7

4 non-working surface damage(weight00286)

typeScore(00063) 3deviationScore(00117) 4locationScore(00105) 6

5 appearance damage(weight01143)


6 physical defect(weight02571)


7 assembly tolerance typeScore(00343) 6(weight01714) deviationScore(01371) 8

Table 7 Ontology and AHP based evaluation results for reuse parts of EOL construction machinery

Results Transition joint Piston rod Bracket of battery Left fender BoomEvaluation score M 77492 91902 68432 27703 44363Reusability level Reuse IV Reuse V Reuse III Reuse I Reuse II

Suggested treatments Reuse part after only simpleremanufacturing process

Reuse partdirectly

Reuse part after complexremanufacturing process Dispose part Recycle part

Figure 8 Quality evaluation results for five reuse parts in web-based system

of EOL construction machinery and builds an ontology andAHP based quality evaluation model The ontology modelis responsible for representing and querying related qualityevaluation of reuse parts The AHP aims to quantify thereusability degree of the parts On the basis of reusabilitydegree rule-based-reasoning method is put forward to getsuggested strategies of reuse parts Compared with otherrelatedmethods [9ndash14] the proposed approach has followingadvantages

(1) Based on the fuzzy AHP Jun Zhou et al [9] putforward a quality evaluationmodel to quantify the reusabilitydegree of the parts Although this approach is able to

realize quality evaluation of reuse parts on the EOL wheelloader it does not propose a universal quality evaluationmodel for reuse parts of all EOL construction machineriesHowever the ontology model in our proposed approach canexpress various reuse parts of all construction machineries indifferent granularity Moreover the classes properties andindividuals in the ontology model and reasoning rules canbe continually updated and enhanced Hence the ontology-based method presented in this paper is highly expansiblewhich can achieve quality evaluation for reuse part of allkinds of engineering machineries and even has potential tobe applied to mechanical products


(2) Tsai Chi Kuo et al [10] proposed a recyclabilityevaluation method based on case-based reasoning whichmakes fully use of experiences knowledge to calculate therecyclability rate of a designed product The experiencesknowledge need to be represented and integrated effectivelybecause of its complex distributed and heterogeneous char-acteristics but Tsai Chi Kuorsquos research does not focus on thisissue In our paper ontology is the solution for this issuebecause ontology not only enables the computer to easilyunderstand and integrate the knowledge but also eliminatesthe ambiguity and heterogeneity of concept expressionFurther the approaches proposed in the references [11ndash14]cannot be shared and reused directly by others Numerousscholars have to reestablish their methods during the studyof the quality evaluation resulting in waste of researchenergy In this paper all represented knowledge can beinterweaved together in the ontology model which achievesthe sharing and reusing of knowledgeThis shared and reusedinformation not only saves research resources but also ishelpful to provide knowledge basis formanufacturersmakingoptimal parts recycling strategy

(3) Although these studies [10ndash14] made some importantconclusions in components recovery domain but no softwareprogram was been developed to show the research resultsand manage the whole recovery process for different usersIn our work a web-based management system is devel-oped through which users can obtain evaluation results andmanage whole processing procedures related to suggestedstrategies conveniently Consequently it is not only good forenhancing the management work efficiency but contributiveto parts reusing information shared by the corporation andthe government

Beyond all that our research can provide manufacturerswith decision support for making profits and protectingenvironment practices such as

(1) When the parts of EOL construction machinery canbe reused directly manufactures will make huge profits andprotect environment well On the one hand reuse partsare able to be applied and assembled directly in other newconstruction machineries by manufactures thus reducingcapital material production machine operation and laborcosts greatly Also reuse parts can be sold to other manufac-turers retailers corporations and waste stations in high salewhich have enormous economic benefit with the increasingmarket demands of the construction machinery On theother hand it is clear that the reuse parts prevent fossil fueland poisonous material polluting our environment due todisposal parts in landfills Moreover reuse parts reduce morecarbon emissions compared with remanufacturing and otherrecycling strategies Hence there are great economic profitsand environmental protection significance in direct reusingof parts in the EOL construction machinery

(2)Once the parts cannot be reused directly the suggestedremanufacturingrecycledispose strategies are helpful tomakemain directions of parts recover ways formanufacturesBased on suggested strategies of all parts on the construc-tion machinery manufactures understand and distinguishclearly which parts should be reused or be remanufacturedor be recycled or be disposed Manufactures are able to

make proper decisions for dealing with all parts of EOLconstruction machinery in the execution of making profitsand protecting environment practices

5 Conclusions

This article proposes an ontology and AHP based qualityevaluation approach for reuse parts of EOL constructionmachinery Ontology model is built for representing evalu-ation information with semantic properties and constructingthe semantic relevance among the various concepts involvedin parts reuse domain thus achieving the integrating shar-ing and reusing of parts reusing knowledge On that basisAHP is put forward to quantify the reusability degree ofthe parts Furthermore combined with ontology rule-based-reasoningmethod based on SWRL is utilized to get suggestedstrategies of reuse parts In addition a web-based system isdeveloped to realize the proposed method and a case studyis used to verify its validity

However this paper does not take into account thedetailed information (such as public behavior economicdevelopment recycling facilities local policies managementactivities financial support new investments carbon emis-sions etc) for reasoning the best suggested actions of reuseparts Moreover how to select the proper recycling actionsis of great importance which can give manufacturers greaterguidance on making profits and protecting environment

In the next step we will make joint efforts on choosingthe more suitable recommended action for parts of EOLconstructionmachinery based on existing study of this paperFirstly many topics about 3R principle (reduce reuse andrecycle) are inserted into the proposed ontology modelSecondly reasoning rules are be updated and enhanced todeduce all proper suggested ways based on much moreprevious experience and knowledge Finally the best recom-mended action is realized based on multiobjective geneticalgorithm which combines the profit maximization with theenvironmental impact minimization

Data Availability

The data used to support the findings of this study areincluded within the article

Conflicts of Interest

The authors declare that they have no conflicts of interest

Acknowledgments

This paper is financially supported by Institute of Electro-hydraulic Control Technology for Engineering Machinery inJilin University and Institute of Security Equipment Technol-ogy in Jilin University

References

[1] S Ahmed S Ahmed M R H Shumon M A Quader HM Cho and M I Mahmud ldquoPrioritizing strategies for sus-tainable end-of-life vehicle management using combinatorial


multi-criteria decision making methodrdquo International Journalof Fuzzy Systems vol 18 no 3 pp 448ndash462 2016

[2] S Sawyer-Beaulieu and E K L Tam ldquoMaximizing AutomotiveParts Reuse Remanufacturing and Recycling Through Effec-tive End-of-Life Vehicle Management A Different Perspectiveon What Needs to be Donerdquo SAE International Journal ofMaterials and Manufacturing vol 8 no 1 pp 118ndash127 2015

[3] J Tian and M Chen ldquoSustainable design for automotiveproducts Dismantling and recycling of end-of-life vehiclesrdquoWaste Management vol 34 no 2 pp 458ndash467 2014

[4] J Gan and L Luo ldquoUsing DEMATEL and Intuitionistic FuzzySets to Identify Critical Factors Influencing the Recycling Rateof End-Of-Life Vehicles in Chinardquo Sustainability vol 9 no 10p 1873 2017

[5] M Hedayati and A Subic ldquoA framework for extended end-of-life vehicle (ELV) recovery rate based on a sustainable treatmentoptionrdquo International Journal of Sustainable Design vol 1 no 4p 381 2011

[6] B Ruffino S Fiore and M C Zanetti ldquoStrategies for theenhancement of automobile shredder residues (ASRs) recy-cling Results and cost assessmentrdquoWaste Management vol 34no 1 pp 148ndash155 2014

[7] P Yi M Huang L Guo and T Shi ldquoA retailer oriented closed-loop supply chain network design for end of life constructionmachinery remanufacturingrdquo Journal of Cleaner Productionvol 124 pp 191ndash203 2016

[8] H Liao Q Deng and Y Wang ldquoOptimal acquisition andproduction policy for end-of-life engineeringmachinery recov-ering in a joint manufacturingremanufacturing system underuncertainties in procurement and demandrdquo Sustainability vol9 no 3 2017

[9] J Zhou P Huang Y Zhu and J Deng ldquoA quality evaluationmodel of reuse parts and its management system developmentfor end-of-lifewheel loadersrdquo Journal of Cleaner Production vol35 pp 239ndash249 2012

[10] T C Kuo ldquoCombination of case-based reasoning and analyticalhierarchy process for providing intelligent decision support forproduct recycling strategiesrdquo Expert Systems with Applicationsvol 37 no 8 pp 5558ndash5563 2010

[11] C-W Tsui and U-P Wen ldquoA hybrid multiple criteria groupdecision-making approach for green supplier selection in theTFT-LCD industryrdquoMathematical Problems in Engineering vol2014 Article ID 709872 13 pages 2014

[12] T-C Kuo S-H Chang and S H Huang ldquoEnvironmen-tally conscious design by using fuzzy multi-attribute decision-makingrdquo The International Journal of Advanced ManufacturingTechnology vol 29 no 5 pp 419ndash425 2006

[13] S Shojaeipour ldquoSustainable manufacturing process planningrdquoThe International Journal of Advanced Manufacturing Technol-ogy vol 78 no 5-8 pp 1347ndash1360 2015

[14] Y Xiang RMo andHQiao ldquoAn EvaluationMethod for BrittleSource of the Key Procedure in Complex PartsrsquoManufacturingrdquoMathematical Problems in Engineering vol 2018 2018

[15] R Elhdad N Chilamkurti and T Torabi ldquoAn ontology-based framework for process monitoring and maintenance inpetroleum plantrdquo Journal of Loss Prevention in the ProcessIndustries vol 26 no 1 pp 104ndash116 2013

[16] S Verstichel F Ongenae L Loeve et al ldquoEfficient data integra-tion in the railway domain through an ontology-basedmethod-ologyrdquo Transportation Research Part C Emerging Technologiesvol 19 no 4 pp 617ndash643 2011

[17] S Chen J Yi H Jiang and X Zhu ldquoOntology and CBRbased automated decision-making method for the disassemblyofmechanical productsrdquoAdvanced Engineering Informatics vol30 no 3 pp 564ndash584 2016

[18] A Zhou D Yu and W Zhang ldquoA research on intelligent faultdiagnosis of wind turbines based on ontology and FMECArdquoAdvanced Engineering Informatics vol 29 no 1 pp 115ndash1252015

[19] H Panetto M Dassisti and A Tursi ldquoONTO-PDM Product-driven ONTOlogy for Product Data Management interoper-ability within manufacturing process environmentrdquo AdvancedEngineering Informatics vol 26 no 2 pp 334ndash348 2012

[20] XHuang J Yi S Chen andX Zhu ldquoAwireless sensor network-based approachwith decision support formonitoring lakewaterqualityrdquo Sensors vol 15 no 11 pp 29273ndash29296 2015

[21] YWang J Yi X Zhu J Luo and B Ji ldquoDeveloping an ontology-based cold chain logistics monitoring and decision systemrdquoJournal of Sensors vol 2015 2015

[22] V Ayala-Rivera P McDonagh T Cerqueus and L MurphyldquoOntology-based quality evaluation of value generalizationhierarchies for data anonymizationrdquo Computer Science 2015

[23] Z Xiaomin Y Jianjun H Xiaoci and C Shaoli ldquoAn Ontology-based Knowledge Modelling Approach for River Water Qual-ity Monitoring and Assessmentrdquo in Proceedings of the 20thInternational Conference on Knowledge Based and IntelligentInformation and Engineering Systems KES 2016 pp 335ndash344UK September 2016

[24] K W Chau ldquoAn ontology-based knowledge managementsystem for flow and water quality modelingrdquo Advances inEngineering Software vol 38 no 3 pp 172ndash181 2007

[25] E Corry P Pauwels S Hu M Keane and J OrsquoDonnell ldquoAperformance assessment ontology for the environmental andenergy management of buildingsrdquo Automation in Constructionvol 57 pp 249ndash259 2015

[26] I Smeureanu and B Iancu ldquoSource code plagiarism detectionmethod using Protege built ontologiesrdquo Informatica EconomicaJournal vol 17 no 3 pp 75ndash86 2013

[27] P Alderfer Clayton ldquoClarfing the meaning of Mentor-Protegerelationshipsrdquo Consulting Psychology Journal Practice ampResearch vol 66 no 1 pp 6ndash19 2014

[28] D Yang R Miao H Wu and Y Zhou ldquoProduct configurationknowledge modeling using ontology web languagerdquo ExpertSystems with Applications vol 36 no 3 pp 4399ndash4411 2009

[29] D Yang M Dong and R Miao ldquoDevelopment of a prod-uct configuration system with an ontology-based approachrdquoComputer-Aided Design vol 40 no 8 pp 863ndash878 2008

[30] X Wang T N Wong and Z-P Fan ldquoOntology-based supplychain decision support for steelmanufacturers inChinardquoExpertSystems with Applications vol 40 no 18 pp 7519ndash7533 2013

[31] I Horrocks P F Patel-Schneider S Bechhofer and D TsarkovldquoOWL rules A proposal and prototype implementationrdquo Jour-nal of Web Semantics Science Services and Agents on the WorldWide Web vol 3 no 1 pp 23ndash40 2005

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needs to be proposed to quantify the reusability degree ofthe parts which determines whether the components canbe reused directly Many researches have been conductedon evaluation approach in construction machinery domainand other industries Jun Zhou et al [9] presented a qual-ity evaluation model to quantify the reusability degree ofthe recycling parts on the EOL wheel loader (a kind ofconstruction machinery) based on the fuzzy AHP Tsai ChiKuo et al [10] put forward a recyclability evaluation methodbased on case-based reasoning and AHP Che-Wei Tsui etal [11] proposed a hybrid multiple criteria group decision-making method based on AHP to assist the manufacturerin choosing four polarizer suppliers Kuo Chang et al[12] proposed a green fuzzy design analysis approach thatcomprises simple and efficient procedures to evaluate productdesign alternatives based on environmental considerationand AHP Shahed Shojaeipour [13] evaluated a sustainablemanufacturing process planning schema through identifyingand quantifying the environmental impacts based on AHPYing Xiang et al [14] proposed the improved fuzzy AHPmethod to analyze and evaluate brittle source of the keyprocedure in increasing the stability during complex partsrsquomanufacturing

Inspired by previouswork AHP is applied into the qualityevaluation approach in this paper however this method hasdrawbacks as follows

(1) It is difficult to propose a universal quality evaluationmodel for reuse parts of all EOL construction machineriesbecause the types of construction machineries are diversesuch as wheel loader crawler excavator bulldozers etcwhich leads to many quality evaluation methods for differentreuse components in various construction machineries

(2) It is hard to reuse the existing quality evaluationmodelof reuse parts Numerous scholars have to reestablish modelsand methods during the study of the quality evaluationresulting in waste of research energy

Ontology is the solution for two defects proposed abovewhich is a mechanism that describes concepts and theirsystem relationships [15 16] On the one hand for the highlyexpansibility of ontology model the classes properties andindividuals in the ontologymodel can be continually updatedand enhanced [17 18] The quality evaluation for reuseparts of different engineering machineries can be realized bychanging the knowledge in the ontology model dynamicallythus realizing a universal quality evaluation approach Onthe other hand ontology can enable the computer to easilyunderstand and integrate the knowledge which achieves thesharing and reuse of quality evaluation knowledge [19 20]In this way existing quality evaluation model of reuse partscan be reused Hence ontology concept will be exploited toovercome two problems mentioned above in this paper

With the rapid development of knowledge technologyontology has been applied into areas like evaluation approachYu-Jun Wang et al [21] developed an ontology-based coldchain logistics monitoring and decision system The systemmakes evaluation and decision support for the monitoredcold chain quality VanessaAyala-Rivera et al [22] introduceda measurement scheme based on ontologies to quantita-tively evaluate the quality of value generalization hierarchies

aiming at producing higher-quality anonymizations Xiao-min Zhu et al [23] proposed an ontology-based knowledgemodel to evaluate water quality through using rule-based rea-soning method Chau KW et al [24] presented an ontology-based knowledge management system for water qualityevaluation Xiao-Ci Huang et al [20] proposed an ontologymodelling approach to evaluate the river water quality basedon rule-based reasoning method Edward Corry et al [25]built an ontology-based performance evaluation model forthe environmental and energy management of buildings

At present combining ontology and AHP is few studiedin the quality evaluation for reuse parts of EOL constructionmachinery and this paper is aimed to fill up this gap

The ontology model is built for representing qualityevaluation parameters of reuse parts with semantic propertiesand constructing the semantic relevance among the variousconcepts involved in quality evaluation domain On thatbasis AHP is put forward to quantify the reusability degreeof the parts of EOL construction machinery to determinewhether the components can be reused directly Furthermorerule-based reasoning method is utilized to realize decisionsupport for reuse measures In addition a web-based systemis developed to assist in managing reuse parts and a casestudy is analyzed to demonstrate the proposed quality eval-uation approach

The remainder of this paper is organized as followsSection 2 mainly focuses on quality evaluation approach forreuse parts of EOL construction machinery Section 3 is thecase study to demonstrate the proposed quality evaluationapproach Section 4 is the discussion about advantages ofproposed methods Section 5 is the conclusions

2 Methodology

This paper mainly focuses on quality evaluation approachfor reuse parts of EOL construction machinery to help usersdetermine whether the components can be reused directlybased on ontology and AHP Figure 1 shows the overallframework of quality evaluation approach

Firstly the ontologymodel is the core part of thismethodwhich is used for representing related quality evaluationinformation of reuse parts with semantic properties Sec-ondly ontology querying based on Sparql (Simple Protocoland RDF Query Language) is executed to query relatedquality evaluation information Thirdly AHP is proposedto quantify the reusability degree of the parts Then SWRLrules are constructed to reason the suggested actions abouthow to deal with reuse parts Finally a web-based system isdeveloped on the visual studio 2017 platform with programlanguage C to assist users in managing reuse parts

21 Structure of Ontology Model The ontology has highlyexpansibility and can enable the computer to easily under-stand the knowledge which makes a formalised definitionto the knowledge of parts reusing All related information ofevaluating parts is expressed and stored into ontology modelProtege [26] software builds ontology model because of itsfriendly interface powerful tools and data checking feature[27] In the Protege editor the construction of ontology




2 Parameters













Reusability Actions

























madeTime

Actions

ReuseLevel

Parameters

Has_Action

Has_CauseHas_Part

Has_Action

Has_Level


deviationScore


deviationScore


deviationScore

NonSurfacetypeScore







deviationScore













failed

successful

AHP







n 1 2 3 4 5 6 7 8RI 0 0 058 09 112 124 132 141



119862119868 = 120582max minus 119899119899 minus 1 (1)


120582max =119899

sum119894=1

(119860119882)119894119899119882119894 (2)



119862119877 = 119862119868119877119868 (3)



119862119894119895 = 119862119894 lowast 119862119895 (4)






important dimension


geometric tolerance


appearance damage

locationScore



typeScore


deviationScore



parts reusability

physical defect

assembly tolerance

locationScore







119872 =119901

sum119894=1

119902

sum119895=1

(119862119894119895 lowast 119878119894119895) (5)














management


Quality evaluation

Users management





Recommended actions


parts








3 Case Study




Reuse_II

Reuse_III

Reuse_V

typeScore

deviationScore

locationScore

AppearanceDamage

PhysicalDefect



Reuse_IV


instantiation


Mission

1Reusability degree








119860 =

[[[[[[[[[[[[[[[[[[[[[

1 5357 55459563

5 137 33413127

573 1 7

7479761

51317 11419164

54347 4 1

49239

5 397 994 1326

5 267 63223 1

]]]]]]]]]]]]]]]]]]]]]

(6)


119882= [01429 00857 02000 00286 01143 02571 01714]119879

(7)





120582max =119899

sum119894=1

(119860119882)119894119899119882119894 = 7 (8)





(01429 00857 02000 00286 01143 02571 01714) (10)


(00536 00893 00171 00686 00750 0125 00063 00117 00105 00336 00454 00605 00756 00454 01361 00343 01371) (11)



119872 = 77492 (12)





4 Discussion




















Reuse partdirectly













5 Conclusions




Data Availability




Acknowledgments


References









































Journal of












Journal of







Volume 2018






Volume 2018











2 Parameters













Reusability Actions

























madeTime

Actions

ReuseLevel

Parameters

Has_Action

Has_CauseHas_Part

Has_Action

Has_Level


deviationScore


deviationScore


deviationScore

NonSurfacetypeScore







deviationScore













failed

successful

AHP







n 1 2 3 4 5 6 7 8RI 0 0 058 09 112 124 132 141



119862119868 = 120582max minus 119899119899 minus 1 (1)


120582max =119899

sum119894=1

(119860119882)119894119899119882119894 (2)



119862119877 = 119862119868119877119868 (3)



119862119894119895 = 119862119894 lowast 119862119895 (4)






important dimension


geometric tolerance


appearance damage

locationScore



typeScore


deviationScore



parts reusability

physical defect

assembly tolerance

locationScore







119872 =119901

sum119894=1

119902

sum119895=1

(119862119894119895 lowast 119878119894119895) (5)














management


Quality evaluation

Users management





Recommended actions


parts








3 Case Study




Reuse_II

Reuse_III

Reuse_V

typeScore

deviationScore

locationScore

AppearanceDamage

PhysicalDefect



Reuse_IV


instantiation


Mission

1Reusability degree








119860 =

[[[[[[[[[[[[[[[[[[[[[

1 5357 55459563

5 137 33413127

573 1 7

7479761

51317 11419164

54347 4 1

49239

5 397 994 1326

5 267 63223 1

]]]]]]]]]]]]]]]]]]]]]

(6)


119882= [01429 00857 02000 00286 01143 02571 01714]119879

(7)





120582max =119899

sum119894=1

(119860119882)119894119899119882119894 = 7 (8)





(01429 00857 02000 00286 01143 02571 01714) (10)


(00536 00893 00171 00686 00750 0125 00063 00117 00105 00336 00454 00605 00756 00454 01361 00343 01371) (11)



119872 = 77492 (12)





4 Discussion




















Reuse partdirectly













5 Conclusions




Data Availability




Acknowledgments


References









































Journal of












Journal of







Volume 2018






Volume 2018



















madeTime

Actions

ReuseLevel

Parameters

Has_Action

Has_CauseHas_Part

Has_Action

Has_Level


deviationScore


deviationScore


deviationScore

NonSurfacetypeScore







deviationScore













failed

successful

AHP







n 1 2 3 4 5 6 7 8RI 0 0 058 09 112 124 132 141



119862119868 = 120582max minus 119899119899 minus 1 (1)


120582max =119899

sum119894=1

(119860119882)119894119899119882119894 (2)



119862119877 = 119862119868119877119868 (3)



119862119894119895 = 119862119894 lowast 119862119895 (4)






important dimension


geometric tolerance


appearance damage

locationScore



typeScore


deviationScore



parts reusability

physical defect

assembly tolerance

locationScore







119872 =119901

sum119894=1

119902

sum119895=1

(119862119894119895 lowast 119878119894119895) (5)














management


Quality evaluation

Users management





Recommended actions


parts








3 Case Study




Reuse_II

Reuse_III

Reuse_V

typeScore

deviationScore

locationScore

AppearanceDamage

PhysicalDefect



Reuse_IV


instantiation


Mission

1Reusability degree








119860 =

[[[[[[[[[[[[[[[[[[[[[

1 5357 55459563

5 137 33413127

573 1 7

7479761

51317 11419164

54347 4 1

49239

5 397 994 1326

5 267 63223 1

]]]]]]]]]]]]]]]]]]]]]

(6)


119882= [01429 00857 02000 00286 01143 02571 01714]119879

(7)





120582max =119899

sum119894=1

(119860119882)119894119899119882119894 = 7 (8)





(01429 00857 02000 00286 01143 02571 01714) (10)


(00536 00893 00171 00686 00750 0125 00063 00117 00105 00336 00454 00605 00756 00454 01361 00343 01371) (11)



119872 = 77492 (12)





4 Discussion




















Reuse partdirectly













5 Conclusions




Data Availability




Acknowledgments


References









































Journal of












Journal of







Volume 2018






Volume 2018
















failed

successful

AHP







n 1 2 3 4 5 6 7 8RI 0 0 058 09 112 124 132 141



119862119868 = 120582max minus 119899119899 minus 1 (1)


120582max =119899

sum119894=1

(119860119882)119894119899119882119894 (2)



119862119877 = 119862119868119877119868 (3)



119862119894119895 = 119862119894 lowast 119862119895 (4)






important dimension


geometric tolerance


appearance damage

locationScore



typeScore


deviationScore



parts reusability

physical defect

assembly tolerance

locationScore







119872 =119901

sum119894=1

119902

sum119895=1

(119862119894119895 lowast 119878119894119895) (5)














management


Quality evaluation

Users management





Recommended actions


parts








3 Case Study




Reuse_II

Reuse_III

Reuse_V

typeScore

deviationScore

locationScore

AppearanceDamage

PhysicalDefect



Reuse_IV


instantiation


Mission

1Reusability degree








119860 =

[[[[[[[[[[[[[[[[[[[[[

1 5357 55459563

5 137 33413127

573 1 7

7479761

51317 11419164

54347 4 1

49239

5 397 994 1326

5 267 63223 1

]]]]]]]]]]]]]]]]]]]]]

(6)


119882= [01429 00857 02000 00286 01143 02571 01714]119879

(7)





120582max =119899

sum119894=1

(119860119882)119894119899119882119894 = 7 (8)





(01429 00857 02000 00286 01143 02571 01714) (10)


(00536 00893 00171 00686 00750 0125 00063 00117 00105 00336 00454 00605 00756 00454 01361 00343 01371) (11)



119872 = 77492 (12)





4 Discussion




















Reuse partdirectly













5 Conclusions




Data Availability




Acknowledgments


References









































Journal of












Journal of







Volume 2018






Volume 2018











important dimension


geometric tolerance


appearance damage

locationScore



typeScore


deviationScore



parts reusability

physical defect

assembly tolerance

locationScore







119872 =119901

sum119894=1

119902

sum119895=1

(119862119894119895 lowast 119878119894119895) (5)














management


Quality evaluation

Users management





Recommended actions


parts








3 Case Study




Reuse_II

Reuse_III

Reuse_V

typeScore

deviationScore

locationScore

AppearanceDamage

PhysicalDefect



Reuse_IV


instantiation


Mission

1Reusability degree








119860 =

[[[[[[[[[[[[[[[[[[[[[

1 5357 55459563

5 137 33413127

573 1 7

7479761

51317 11419164

54347 4 1

49239

5 397 994 1326

5 267 63223 1

]]]]]]]]]]]]]]]]]]]]]

(6)


119882= [01429 00857 02000 00286 01143 02571 01714]119879

(7)





120582max =119899

sum119894=1

(119860119882)119894119899119882119894 = 7 (8)





(01429 00857 02000 00286 01143 02571 01714) (10)


(00536 00893 00171 00686 00750 0125 00063 00117 00105 00336 00454 00605 00756 00454 01361 00343 01371) (11)



119872 = 77492 (12)





4 Discussion




















Reuse partdirectly













5 Conclusions




Data Availability




Acknowledgments


References









































Journal of












Journal of







Volume 2018






Volume 2018



















management


Quality evaluation

Users management





Recommended actions


parts








3 Case Study




Reuse_II

Reuse_III

Reuse_V

typeScore

deviationScore

locationScore

AppearanceDamage

PhysicalDefect



Reuse_IV


instantiation


Mission

1Reusability degree








119860 =

[[[[[[[[[[[[[[[[[[[[[

1 5357 55459563

5 137 33413127

573 1 7

7479761

51317 11419164

54347 4 1

49239

5 397 994 1326

5 267 63223 1

]]]]]]]]]]]]]]]]]]]]]

(6)


119882= [01429 00857 02000 00286 01143 02571 01714]119879

(7)





120582max =119899

sum119894=1

(119860119882)119894119899119882119894 = 7 (8)





(01429 00857 02000 00286 01143 02571 01714) (10)


(00536 00893 00171 00686 00750 0125 00063 00117 00105 00336 00454 00605 00756 00454 01361 00343 01371) (11)



119872 = 77492 (12)





4 Discussion




















Reuse partdirectly













5 Conclusions




Data Availability




Acknowledgments


References









































Journal of












Journal of







Volume 2018






Volume 2018










Reuse_II

Reuse_III

Reuse_V

typeScore

deviationScore

locationScore

AppearanceDamage

PhysicalDefect



Reuse_IV


instantiation


Mission

1Reusability degree








119860 =

[[[[[[[[[[[[[[[[[[[[[

1 5357 55459563

5 137 33413127

573 1 7

7479761

51317 11419164

54347 4 1

49239

5 397 994 1326

5 267 63223 1

]]]]]]]]]]]]]]]]]]]]]

(6)


119882= [01429 00857 02000 00286 01143 02571 01714]119879

(7)





120582max =119899

sum119894=1

(119860119882)119894119899119882119894 = 7 (8)





(01429 00857 02000 00286 01143 02571 01714) (10)


(00536 00893 00171 00686 00750 0125 00063 00117 00105 00336 00454 00605 00756 00454 01361 00343 01371) (11)



119872 = 77492 (12)





4 Discussion




















Reuse partdirectly













5 Conclusions




Data Availability




Acknowledgments


References









































Journal of












Journal of







Volume 2018






Volume 2018












120582max =119899

sum119894=1

(119860119882)119894119899119882119894 = 7 (8)





(01429 00857 02000 00286 01143 02571 01714) (10)


(00536 00893 00171 00686 00750 0125 00063 00117 00105 00336 00454 00605 00756 00454 01361 00343 01371) (11)



119872 = 77492 (12)





4 Discussion




















Reuse partdirectly













5 Conclusions




Data Availability




Acknowledgments


References









































Journal of












Journal of







Volume 2018






Volume 2018


























Reuse partdirectly













5 Conclusions




Data Availability




Acknowledgments


References









































Journal of












Journal of







Volume 2018






Volume 2018















5 Conclusions




Data Availability




Acknowledgments


References









































Journal of












Journal of







Volume 2018






Volume 2018















































Journal of












Journal of







Volume 2018






Volume 2018








an ontology and ahp based quality evaluation approach for

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