Computers in Industry 64 (2013) 720–731

Product similarity assessment for conceptual one-of-a-kind productdesign: A weight distribution approach

B.M. Li, S.Q. Xie *

Department of Mechanical Engineering, University of Auckland, Auckland 1142, New Zealand

A R T I C L E I N F O

Article history:

Received 6 March 2013

Accepted 3 April 2013

Available online 13 May 2013

Keywords:

Case retrieval

Generic product model

Weight distribution model

Product conceptual design

One-of-a-kind production

A B S T R A C T

Case-based reasoning (CBR) is a promising methodology for assisting conceptual product design. The

efficiency of case retrieval determines the quality of design. In one-of-a-kind product design, customer

activities are increasingly involved. Under the circumstances customized product design results in the

increase of product variety, which produces a large case library. This brings difficulties in managing

customized product families, and also results in issues in retrieving similar cases. In the scope of our

knowledge, Only limited studies attempt to research case retrieval by addressing these OKP practice for

its conceptual product design. Moreover, customer requirements (CRs) are not fully interpreted to guide

this process at the preliminary design stage. To cope with these issues, approaches for elicitation of CRs

and retrieving appropriate cases play essential roles in successful case retrieval for OKP. This paper

proposes a modularized generic product model (MGPM) for managing OKP product families. This

structured generic product model can represent a significant number of product/module/component

variants in a single product model. Also, the weight distribution model (WDM) is developed for assign

weighting factors of CRs to multi levels of product physical configuration. For similarity assessment, a

weighted distance-based algorithm is developed to calculate the degree of similarity between the target

case and the reference cases. A case study on the fruit chute system is carried out to prove the efficiency

and industrial applicability of the proposed approach. The result shows that the propose approach is

capable of effectively retrieving similar product/part variants according to specified CRs.

� 2013 Elsevier B.V. All rights reserved.

Contents lists available at SciVerse ScienceDirect

Computers in Industry

jo ur n al ho m epag e: ww w.els evier . c om / lo cat e/co mp in d

1. Introduction and related works

Nowadays, the survival and success of one-of-a-kind produc-tion (OKP) companies largely depend on their ability to developnewer, better and more innovative products within a shortperiod of time [1,36,37,38,39]. For many OKP companies, it iswidely accepted that their design practices rely heavily on pastdesign experience and knowledge, instead of designing every-thing from scratch [2–4]. CBR methodology is built based on thisconcept, which is proved to be a promising methodology onassisting product design by adapting previously successfulsolutions to current problems. It has been employed andintegrated with many techniques to assist product design, e.g.,fixture design, micro-electro-mechanical systems (MEMS) de-sign, product assembly design [5–7]. Case retrieval is the firstand most crucial process in product design reuse as retrieveddesigns may influence or determine the quality of the new design[4,8,9].

* Corresponding author. Tel.: +64 9 3737599x88143; fax: +64 9 3737479.

E-mail address: s.xie@auckland.ac.nz (S.Q. Xie).

0166-3615/$ – see front matter � 2013 Elsevier B.V. All rights reserved.

http://dx.doi.org/10.1016/j.compind.2013.04.001

1.1. Customer requirements interpretation

In OKP, product design is a customer-centric activity, whichinvolves customer influences and preferences significantly. There-fore, the fundamental objective is to maximize customer satisfac-tion on product configuration and design quality. Similarly,successful case retrieval relies on comprehensive elicitation ofCRs. According to [10–12], CRs consist of two main categories,quantitative and qualitative requirements as shown in Fig. 1.Normally, functional requirements (FRs) and design parameters(DPs) are represented in quantitative and explicit form. They canbe characterized and quantified in terms of functional, technologi-cal, and structural views. On the contrary, customer preferences(CPs) are qualitative and subjective requirements which aredifficult to characterize and elicit due to their impreciseness andambiguity. In the context of OKP product design, requirements andpreferences may vary from one customer to another. Therefore, theunderstanding of CRs plays an essential role in retrieving the rightproduct designs.

Several techniques and approaches have been developed tointerpret CRs, such as quality functional deployment (QFD),analytic hierarchy process (AHP), fuzzy AHP and so on. QFD is a

Fig. 1. Classification of customer requirements in product design.

B.M. Li, S.Q. Xie / Computers in Industry 64 (2013) 720–731 721

technique for translating CRs into the appropriate productengineering characteristics [13]. AHP is proposed to determinethe importance rates of CRs [14]. Based on AHP, the fuzzy AHP isdeveloped by combining with fuzzy logical theory to deal withuncertainties in CRs [15]. However, these approaches heavilydepend on human subjective judgement during interpretation ofCRs and determination of importance rates of them. In addition,they ignore the elicitation of subjective CPs which are alsoimportant for customized product design.

1.2. Case retrieval

In case retrieval, similarity assessment plays a vital role inevaluating similarities among cases. Many similarity measureshave been proposed, in which measures based on pair-wisecomparison were of great popularity [16,17]. Pair-wise comparisonmeasures the similarities between new and prior cases throughcomparing pairs of their parameters. In order to take full advantageof computing power, a vector-based method was developed basedon pair-wise comparison. In this method, a case is represented by avector composed of a set of attributes [18,19]. It was used in pair-wise comparison for similarity assessment. Wang and Rong [18]represented features of work-pieces in a vector-based method forcase retrieval of welding fixture design based on CBR methodology.Zhang et al. [19] proposed a vector-based method for design caseindexing and retrieval. Both cosine-based similarity measures anddistance-based measures are popular in vector-based similarityassessment.

Although the abovementioned approches are proved to beeffective on similarity assessment, limitations on precision alsoexist due to the fact that not all parameter bears the samesignificance. Since attributes in a vector are of different scales inpractice, the standardization of them is very important tosimilarity measurement. Otherwise, attributes measured withlarge valued units will dominate the result, while attributes thatare measured with small valued units will contribute very little. Toaddress this issue, weighting factors are needed for each pair ofparameters to form weighted measures [19]. The weights of theparameters represent their significance to the product. Theperformance of similarity measures strongly relies on the typeand importance of each parameter represented in the case.

Therefore, the weighting factors by interpreting CRs play anessential role in improving the effectiveness of pair-wise similarityassessment.

In OKP, as CRs vary from one customer to another, the weightsof attributes for case retrieval are also different from time to time.In order to generate weighting factors for weighted similarityassessment, extensive research has been carried out in CRsinterpretation and elicitation methodologies [13,14,20]. Thesemethods attempted to interpret CRs for assisting case retrieval anddistributing weights of CRs for similarity measures. For instance,Xia and Wang [11] adopted generic bill-of-material (GBOM) toconstruct the mapping mechanism for determining weights of CRs.By decomposing product into different levels of abstraction, CRsare mapped directly to the items of product family architecture.

1.3. Product data modelling

Management of existing designs preserves product data andknowledge with expert rationales and experience [21]. Thus, itreduces the negative impact from the losses of experts, and enablesagile OKP [3,22]. Although benefits are obvious, it is a challengingwork to abstract and model OKP product data and knowledge. Thecause is that satisfying a wide variety of CRs brings about a largenumber of various products in OKP. As a consequence, an OKPcompany usually have a large design repository containing a greatnumber of similar products. It poses difficulties on manage thesehighly customized product as data redundancy exists in OKPproduct data modelling.

Many studies have been made on modelling product familydata such as the bill-of-material (BOM), the GBOM, the productstructure model (PSM), the product configuration model (PCM),and so on [11,23–25]. In these methods, the BOM is not feasible formodelling large product families as it contains much redundancyin data and knowledge. Based on the classic BOM, the GBOMprovides a generic way to represent a large number of variantswithin a product family by abstracting from the detaileddifferences between them [26]. The generic product modellingapproaches have the following advantages on solving issues whenmanaging a customized product family. Thanks to their knowledgeoriented data structure, they avoid product data redundancy inproduct modelling, provide maximal product variations to satisfy

B.M. Li, S.Q. Xie / Computers in Industry 64 (2013) 720–731722

various CRs, and enable intelligent applications [27]. However,Naciri et al. [24] pointed out that the GBOM is an inadequate way tomanage customized product data as ‘‘these customized productsare produced just once or in a very few replicates’’. This is causedby OKP characteristics on small order size and high productvariety. Therefore, in authors’ point of view, not only the GBOM butalso all other unstructured generic product models are incapable ofeffectively managing OKP product data for case retrieval purpose.

In addition, the increase on product variety also posesdifficulties on finding a match from existing designs at the productlevel. However, current reuse-oriented case retrieval approachesfocus on retrieving and evaluating similar designs at either theproduct level or the part level only [28–32]. It means the efficiencyis limited as they are incapable of dealing with case retrieval atmulti levels. For example, in [29–32], they can only retrieve partssimilar to the required features. Therefore, case retrieval in OKPshould be extended from a single level to multi levels. Thedecomposition of the product configuration can enable this. Byextending the traditional GBOM, the product configuration model(PCM) was developed to support the customized product designproblem [6,33]. Zhang et al. [33] claimed that the PCM is able toprovide an effective means for decomposing and analysing theproduct conceptual structure. In [33], a product configurationdesign approach is proposed based on the PCM to supportcustomized product design. Nie et al. [6] proposed a rapid lockingassembly variant design system based on PCM and CBR with theaim of rapid response to requirements of mechanical productcustomization. These studies proved the feasibility of representingthe product conceptual configuration in a hierarchical multi-levelmodel. The PCM also allows mapping between CRs and the productphysical structure, which improves the understanding of relation-ship among them.

1.4. Issues

To sum up, limited studies attempt to develop case retrievaltechniques by addressing the following issues in OKP practice forproduct design. Our paper will dedicate to solve these issues inorder to facilitate efficient case retrieval in OKP.

� Successful case retrieval is impossible without full interpretationof CRs. However, in most currant studies, subjective CPs areignored although they are as important as other CRs in productdesign, especially for OKP design. Therefore, the elicitation andquantification of CPs is urgent for product design and caseretrieval.� For OKP products, products with greater similarities result in

difficulty on the accuracy and efficiency of case retrieval. Hence,it is harder to search the proper and optimal solutions to meetthe given CRs.� Currant generic product modelling approaches are inadequate

for modelling OKP products. A structured generic product modelis needed to improve the efficiency of case indexing and retrieval.

This paper is structured as follows. Section 1 presented theintroduction of OKP characteristics and state of the art oftechnologies and works on product data modelling and caseretrieval. The issues to be addressed for OKP case retrieval werealso pointed out. At the beginning of Section 2, a process for OKPcase retrieval is proposed. Then, a modularized generic productmodel (MGPM) that facilitates OKP product data modelling andcase retrieval is detailed for the first time. Finally, a weightdistribution model (WDM) method is presented to interpret CPs forassisting case evaluation. WDM combined with a hierarchicalstructured PCM to have weighting factors distributed to productlevel, part level, and parameter level. In Section 3, a case study is

carried out to demonstrate the effectiveness of the proposedMGPM and WDM on OKP data modelling and case retrieval.

2. Methodology

In this section, a process of case retrieval for OKP is firstrepresented. Then, a structured generic data model, the modular-ized generic product model (MGPM), is proposed for managingOKP product family. Finally, a new weight distribution approach isproposed to characterize and elicit CPs.

2.1. Process of case retrieval in OKP

Currant approaches for case retrieval normally ignore theimpact of customer subjective preferences, which is the reasonthat they have limitations on retrieving cases for OKP designs. Inorder to bridge this gap, this paper presents a case retrieval andevaluation process by taking both quantitative CRs and qualitativeCRs into consideration.

In our proposed case retrieval and evaluation process, bothquantitative and qualitative CRs are characterized and interpretedto facilitate case retrieval for OKP product design. As shown inFig. 2, this process is divided into the primary retrieval stage andthe evaluation stage. In the primary retrieval stage, DPs and FRs areinterpreted into product specifications and parameterized speci-fications for case retrieval. The product function is decomposedinto multi-levels, i.e. the module level, the component level, andthe attribute/parameter level from top to bottom. By referring tothe predefined product configuring rules, a design configuration isidentified. By comparing similarities, a set of modules (unit,building-blocks) is also identified to meet quantitative CRs in bothfunctional and structural views. Each module consists of a range ofcomponents, and it is able to realize its sub-functions in themodule level. Therefore, the product function is fulfilled by thecombination of them. Next, for each component in each module, anumber of eligible candidates are retrieved primarily by referringto its design specifications and sub-functions in the componentlevel. Section 2.3 will explain this stage in more detail.

In the evaluation stage, CPs and their weights are decomposedand elicited by the proposed weight distribution approach. Thenthey are mapped from weight distribution model (WDM) toproduct configuration model (PCM). Weights of CPs are calculatedby the pre-defined estimation equations. Through this process, themost suitable component can be selected from the cases retrievedin the primary retrieval stage. The elicitation of CPs contributes forevaluating retrieved candidates, and maximizing customer satis-faction. This stage will be detailed in Section 2.4.

2.2. Modularized generic product model

In this paper, a structured generic product data model, themodularized generic product model (MGPM), is proposed torepresent OKP product family. As a genetic product model, theMGPM can deal with the issue on representing and managing OKPproducts and design knowledge. In addition, MPGM is generatedby modularizing the generic product model (GPM). Therefore,besides of the generic product layer and the generic componentlayer, MGPM contains one more generic layer, the generic modulelayer. In the MPGM, products can be grouped into functional designmodules according to their different functionalities. Modulevariants of each module realize a same set of functions althoughthey may vary in other views (e.g. the physical structure view).

The single-stage gear reducer is taken as an example to explainhow the MPGM manages the product families and variations(Fig. 3). There are three generic functional design modules for thisproduct, which are the driving gear module, the driven gear

Fig. 2. Process of primary retrieval and evaluation for OKP.

B.M. Li, S.Q. Xie / Computers in Industry 64 (2013) 720–731 723

module, and the box module. Each of them consists of one or moregeneric components. For instance, the box base and the box coverare the two generic components in the box module.

The ‘‘part-of’’ relationship represents the parent–offspringrelationship between two adjacent generic levels, such as theproduct with its modules. The ‘‘type-of’’ relationship representsthe connection between generic items (in the rounded rectangles)and their design variants (in the white rectangles). Items in orange

Fig. 3. The modularized generic product model for cus

rectangles are optional items, and the dash line between themdenotes the ‘‘OR’’ relationship as only one of them can be include ina single module. For example, the combination of a shaft and a gearcan transmit torque and speed in the driven gear module, while agear shaft component can also realize the same function. However,there is only one of them in a driven gear module. Therefore, theyare two optional items, and the relationship between them are the‘‘OR’’ relationship as shown in Fig. 3.

tomized gear reducer example (inspired by [23]).

B.M. Li, S.Q. Xie / Computers in Industry 64 (2013) 720–731724

2.3. Primary case retrieval

According to the proposed process in Section 2.1, the primarycase retrieval stage consists of the following tasks: (1) interpreta-tion of DPs and FRs; (2) identification of design modules; and (3)retrieval of eligible candidates for each module’s component.

Many function modelling approaches have been proposed toanalyse product FRs, e.g. function-behaviour-state (FBS) model[34]. In these approaches, the function of a higher level is brokendown into a series of sub-functions of a lower level. This action isnamed the function decomposition, which provides an explicitway to improve the understandings of FRs. In the functiondecomposition tree, the main function is split into several sub-functions in the lower levels as shown in Fig. 4. The main functionof a speed reducer is to reduce its input speed, which is firstlydecomposed into two sub-functions, to slow down input speed andto contain components. Then, each sub-function is furtherdecomposed into a set of sub-functions of a lower level. Forinstance, ‘‘to slow down input speed’’, a sub-function of the mainfunction is decomposed into three sub-functions, which are ‘‘toconvert speed to torque’’, ‘‘to transmit toque’’, and ‘‘to converttorque to speed’’. By realizing them, the ‘‘to slow down speed’’function can be fulfilled at the same time.

Fig. 4 also describes the mapping between the functions and theproduct structure to illustrate the process of the identification ofdesign configuration. In the mapping process, functions in thefunction decomposition tree are mapped to generic product itemsin the MGPM, which means relations among FRs and productphysical elements (modules or components) are obtained. Ulrichand Eppinger [35] claimed that the product architecture/configu-ration can be identified by mapping between product functionsand physical components. In another words, the process of productconfiguring is a process of identifying design modules andcomponents that fulfil FRs. This is a ‘‘many-to-many’’ mappingas each function is normally realized by several components fromdifferent functional modules. For example, the function of‘‘position gears’’ is realized by the input shaft, the output shaft,the box cover, and the box base from three different designmodules. Through the mapping process, each generic componentcan be represented by its allocated functions. Unlike theconventional generic product models, the modularization ofgeneric items (components) in the MGPM offers an acceptableway for case indexing and customized product configuring in thefunctional view.

With the identified product modules, DPs can be determined bythe predefined rules, constraints, and equations. For example, the

Fig. 4. Mapping between function decomposition and gen

main DPs for a gear reducer product include the transmission ratio,the transmission power, and the input speed. They determineother detailed DPs such as the output speed, the two teethnumbers, the module number of gears, and so on. The detailedexplanation and case study will be given in Section 3.

2.4. Case evaluation

For case evaluation, this paper proposes a weight distributionapproach in order to choose the most suitable cases that maximizecustomer satisfaction by eliciting and characterizing CPs. As shownin Fig. 5, in the evaluation stage, the weights distribution process ofCPs starts from WDM to different levels of the PCM. In the WDM,the importance of CPs are presented by a set of weighting factors,W(CPs) = {W1, W2, . . ., Wn}, where Wi denotes the ith weightingfactors. Each of them can be split into several sub-weightingfactors. For example, the nth weight Wn can be represented byWn = {Wn1, Wn2, . . ., Wnk}, where nk is corresponding to the kth sub-weights of the nth weights. In this model, CPs as well as theircorresponding weights can be decomposed and mapped to productphysical structure.

In the PCM, the product A is made up of a set of modulesidentified in the primary retrieval stage, A = {M1, M2, . . ., Mm}, andeach module is denoted by a set of its parts, Mi = {Pi1, Pi2, . . ., Pip},where ip denotes the pth part representing the mth module. Theweights from WDM are mapped to PCM from product leaves(parts) to its root (product), and the arrows in Fig. 5 show thedirection of distribution. Through this mapping process,the importance of CPs can be distributed to different levels ofthe product configuration (i.e. the product level, the componentlevel, and the parameter level).

At the bottom-right of Fig. 5, the distributed weights of CPs aremapped to a part (P21) from its parameter level. This part isrepresented by a vector of four design attributes, P21 = {A1, A2, A3,A4}. The weights of these attributes are equal before the weightdistribution as we assume all of them bear the same importancerate ‘‘a’’. Therefore, the original weighting vector for retrieving thispart is (a, a, a, a). After mapping, two sub-weights W12 and W2 thatcomes from the WDM are distributed to the two design attributesA1 and A3, respectively. The values of DP1 and DP3 represent theadditional importance rate of A1 and A3. Thus, the weights for A1

and A3 are changed while the rest ones are kept the same. The newweighting vector for evaluating part P21 is revised toW(P21) = (a + W12, a, a + W2, a).

There are several rules for implementing this distributionprocess: (1) design attributes representing a part/module should

eric product configuration model for speed reducer.

Fig. 5. Weight distribution approach and the process of distributing CPs weights to product physical elements.

B.M. Li, S.Q. Xie / Computers in Industry 64 (2013) 720–731 725

be unique and irrelevant to each other, otherwise, it is impossibleto split and assign weights to each attribute effectively; (2) in theWDM the sum of weights or sub-weights equals to 1; and (3) theweight of each part equals the sum of the weights of its designattributes. For instance, the weight of P21 is the sum of all attributesin W(P21), which equals 4a + W12 + W2. For its parent M2, theweight is contributed by the weights of P21 and P22. This approachnot only provides a way to identify weights for case retrievalaccording to different FRs, but also offers a method to retrieve partsas well as components of a product. The detailed demonstrationwill be given in Section 3.

In this paper, the Euclidean distance-based measure is usedbecause the cosine-based measures are invariant to scales ofdifferent attributes. Eq. (1) is the distance-based dissimilarityfunction, in which E(X, Y) is the distance/dissimilarity between X

and Y. X denotes the vector of the target cases, while Y representsprevious cases.

Distance ¼ EðX; YÞ ¼

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiXn

i¼1

Xi � Yi

maxðXi; YiÞ

� �2vuut (1)

where X = (Xi|i = 1, . . ., n), Xi denotes the ith attributes of case X.Y = (Yi|i = 1, . . ., n), Yi denotes the ith attributes of previous case Y.The higher the result is, the larger the dissimilarity level is.Moreover, a weight vector W is defined prior to measuring thesimilarity, of which the attributes are the importance rates of FRsaccording to individual customers, W = (W1, W2, . . ., Wn). Afternormalization, we get the normalized weighting vector w,

w ¼ wPni¼1 wi

¼ ðw1; w2; . . . ; wnÞ (2)

After taking weighting factors of attributes into consideration,the new weighted formula is formed based on the distance-basedfunction (Eq. (2)). A threshold number d is predefined to screen theunqualified cases and reduce the number of cases in primaryretrieval, d 2 [0, 1]. If Sim(X, Y) > d, case Y is retrieved as one of the

potential cases.

Distance ðX; YÞ ¼

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiXn

i¼1

wi �Xi � Yi

max ðXi; YiÞ

� �2vuut (3)

3. Case study

This section presents a case study on the fruit chute system todemonstrate the proposed MGPM and the case retrieval andevaluation approach for OKP. The fruit chute system is animportant parts of the fruit sorting system. Its functionality is todeliver fruits and vegetables to outlets without damage. Thisproduct is a typical OKP product as it has OKP characteristics suchas small order size and high variety. Firstly, through this case study,the conceptual configuration of a chute system is identified byanalysing quantitative CRs. Then, the primary case retrieval stage isapplied. Finally, the proposed weight distribution approach isillustrated for the case evaluation stage. Results are analysed toprove the effectiveness of the proposed approach.

3.1. Primary case retrieval

With the graphical user interfaces (GUIs) shown in Fig. 6, CRsare collected and itemized. For a fruit chute system, the DPsinclude the number of lanes, the gap between two adjoining lanes,the gap of each side, and the total width of the delivery line. The FRsis to transfer and deliver produce to belts running in a paralleldirection without causing damage. The CPs are the preferencestowards the product volume and mass. The importance rates of theCPs are also pointed out by customer, which are very importantand extremely important respectively.

Generally, a chute system consists of three main modules, i.e.the chute module, the brush module, and the frame module. Eachmodule contains a number of variants consisting of differentcomponents to meet diverse CRs. Therefore, diverse systemfunctions can be generated by combining different module

Fig. 6. Customer requirements for fruit chute system design.

B.M. Li, S.Q. Xie / Computers in Industry 64 (2013) 720–731726

variants. For example, the delivery direction can determine themodule configuration. For the same fruit kind (golden kiwifruit), asweep component in the chute module is needed to guide fruits tothe perpendicular direction, and the opening angle of the sweep isdetermined by fruit varieties.

The function–structure model proposed in Section 2 is used todeal with the complexity of design configuring and identify systemconfiguration according to the input FRs (Fig. 7). The chute modulecontains the chute body for landing and sliding fruits, the foamlayer for absorbing landing shocks for keeping fruits from collision,and the chute holder for fasten chutes. The brush module providesa more gentle way of lowering fruits. In this module, the brush

Fig. 7. Mappings between function decompo

body is used for gently guiding fruit to exit chute, and the brushshaft is used for driving the brush body in rotation motion. Theframe module consists of three major sheet metal parts (the backframe and two side frames) to hold up other modules andcomponents in place. Therefore, the relations between functionsand product items are mapped as shown in Fig. 7. Throughanalysing the FRs, the functions for modules and components areobtained for identifying design configuration and retrievingsuitable modules primarily.

Fig. 8 shows the variants of the chute module and the functionsthey can achieve. On the top of this figure, six basic design variantsthat realize different functions are given. As analysed in Fig. 7, the

sition tree and MGPM for chute system.

Fig. 8. Variants of the chute module and their function vectors.

B.M. Li, S.Q. Xie / Computers in Industry 64 (2013) 720–731 727

functions for the chute module include ‘‘to change deliverdirection’’, ‘‘to avoid collision after exit’’, ‘‘to soft landing’’, ‘‘toavoid falling from side’’, and ‘‘to work with brushes’’ (as listed inthe table at the bottom of Fig. 8). Each column of this tablerepresents one function to be satisfied, and each row denotes onevariant and its function vector FV = {FVi} (i = 1, 2, . . ., n). In our casen = 8. Data type of each function is also given. For the Boolean datatype, ‘‘0’’ indicates this variant is incapable of providing thisfunctionality, while ‘‘1’’ denotes the opposite. For example, thefunction vector of variant 2 is (1, 0, 90, 1, 0, 0, 1, 0), which meansthis variant can (1) allow change deliver direction; (2) avoidcollision between fruits after exit this chute; and (3) avoid fruitfalling from side. As such, it is designed for fruits not that delicatesuch as standard kiwi fruit, with a perpendicular deliver direction.

The required FRs is to design a chute system for goldenkiwifruits with the parallel delivery direction (Fig. 6). The golden

Fig. 9. Mapping CPs with propose

kiwifruit is more delicate than the standard kiwifruit, whichrequires the soft landing functionality and the gentle loweringfrom the brush module. Therefore, the function of the requiredchute module should include (1) soft landing; (2) avoid falling fromside; and (3) work with brush. As such, its function vector shouldbe {0, 0, 0, 0, 1, 0, 1, 1} or {0, 0, 0, 0, 0, 1, 1, 1}. Obviously, the variant5 is eligible for satisfy the required FRs.

The functions can be further decomposed when comes to morecomplex FRs. As a consequence, the accuracy of moduleidentification will be improved. On the other hand, the computa-tion time will be increased as the size of function vectors will belarger.

The overall DPs captured in Fig. 6 are calculated to get DPs foreach component/module by pre-defined formula and constraints.Taking the chute module as an example, the number of this modulemay vary from 2 to 13, which depends on the number of delivery

d weight distribution model.

Table 1The weight distribution based on WDM.

Main parts Volume (V = 0.3333) Mass (M = 0.6667)

L = 0.5774 W = 0.5774 H = 0.5774

Chute body 0 0.3684 0 0.025

Chute holder 0.2456 0 0 0.1

Brush body 0 0.3684 0.7368 0.1

Brush shaft 0.3684 0 0 0.1

Frame left 0.2456 0.3684 0 0.0667

Frame right 0.2456 0.3684 0 0.0667

Frame back 0.2456 0.3684 0 0.0667

B.M. Li, S.Q. Xie / Computers in Industry 64 (2013) 720–731728

lanes. Its width relies on the gap between two adjoining lanes. Forthe chute holder component, one nail plate is correspondent to onechute module. The length of chute holder is determined by thewidth of the entire delivery line. The DPs of the associatedcomponents in each module can also be calculated in this way.

3.2. Case evaluation

Unlike quantitative and objective CRs, it is difficult for quantifyCPs as they are subjective and qualitative and may vary from onecustomer to another. In order to cope with this issue, theimportant rate of each CP is set from 1 to 5. The very importantand the extreme important scores 2 and 4, respectively. Accordingto the input CRs in Fig. 6, the original weight vector of CPs is (2,4). Because the sum of all CPs weights on each level should be 1,the weight vector (volume, mass) is (0.3333, 0.6667) afternormalization.

According to the weight distribution approach, the WDM andthe PCM for this chute case is developed, and the mapping processbetween them is shown in Fig. 9. The objective is to obtain theweighting vectors for parts/modules. The product configuration, asidentified in the primary retrieval stage, consists of seven mainparts, which are the chute body, the chute holder, the brush body,the brush shaft, two side frames and one back frame. Based on thisconfiguration, the following equations can be used to estimate theproduct volume and mass. They also assist analyse relationshipsbetween CPs and their weights for distribution. For example, thevolume preference towards the required product configuration canbe estimated by Eqs. (4)–(7).

Volume ¼ Height � Width � Depth (4)

Height ¼ HCH þ 2DSF (5)

Width ¼ HCH þ 2 �

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiWBB

2

� �2

� WBB

2� DCB

� �2s

(6)

Depth ¼ max ðDBB; WSF ; DCHÞ (7)

Mass ¼ 5 � MassCB þ MassCH þ MassBS þ 2 � MassSF þ MassBF (8)

Fig. 10. Shaft variants and

where Hname, Wname, Dname denotes the height, width, and depth ofthe part name, respectively. For instance, DBB denotes the depth ofbrush body part, HCB is the height of chute body, and WFL representsthe width of left frame.

In Eq. (4), the volume of this product is calculated by theresult of multiply of the height, the width, and the depth.Therefore, the assigned weight for each of them (Height, Width,Depth) equals to the cubic root of the weight of the volumepreference (0.3333). Then, with the assigned weights, the height,the width, and the depth calculated by Eqs. (5) and (6) transfertheir assigned weights to component parameters. For instance,the height is determined by the height of the chute holder andthe depth of two side frames. As such, the weight assigned toheight is transferred to the corresponding component param-eters. In the same manner, the weight of the mass preference(0.6667) is split into several sub-weights and distributed to eachpart as the total product mass depends on the weights of itsparts (Eq. (8)). Table 1 gives the weight distribution result basedon the proposed WDM approach. In this table, the columnsdenote the weights for component parameters, e.g., H is forheight, while D is for depth. The distributed weights can be usedto evaluate all chute body component/module retrieved in theprimary case retrieval stage.These weights for parts can bedistributed to the lower level of PCM, the attribute level. Wetake brush shaft as an example to demonstrate the evaluationprocess. Besides of basic dimensional parameters such as shaftdiameters, total length, there are some other kinds of designparameters. For example, shafts normally provide three kinds of

important parameters.

Fig. 11. Weight distribution and normalization for the brush shaft to produce weighting vectors.

B.M. Li, S.Q. Xie / Computers in Industry 64 (2013) 720–731 729

motion, rotation, reciprocation, or oscillation. In our case, thefunctionality of the brush shaft component is to drive the brushbody component mounting on it to do rotation motion. Fig. 10(a)gives several shaft designs that are qualified for providing thisfunctionality. Due to the large number of part variants undereach design, only the basic features of them are shown here. The

Fig. 12. Screen shot of GUI for ou

important DPs as well as their units and descriptions are listedin Fig. 10(b).

In our case, volume of the brush module mainly depends on theouter diameter of the brush body. Thus, we only considered themass of the brush shaft in its case evaluation. On the top of Fig. 11,two equations for estimating the part mass are shown. One is for

tput retrieved components.

Fig. 13. Design configurations of the chute system with the retrieved parts/modules.

B.M. Li, S.Q. Xie / Computers in Industry 64 (2013) 720–731730

solid shafts, while another is for hollow shafts. In Fig. 10, the design2# is a hollow shaft example, and other designs are all solid shafts.Results of weight distribution from brush shaft to its attributes areas shown in Fig. 11. The normalized results are present in thebottom. Shaft designs with different step numbers have differentresults. For instance, the part variants in the design 1#, 4#, and 7#should be evaluated by the normalized weighting vector w ¼ð0:0929; 0:0929; 0:0929; 0; 0:1966; 0:1966; 0:1966; 0; 0:1316; 0Þ astheir step number is 3. By applying Eq. (3) and the weightingvector w, the dissimilarities (distance) are calculated between therequired case and all the retrieved case. After the evaluation, thesuitable brush shaft case is a variant in design 2#. The final result ofthe retrieved brush shaft is shown in Fig. 13.

The degree of similarity is assessed after capturing the CRs anddistributing weights of the CPs to the component level. By repeatingthe part retrieval and evaluation process similar parts can beretrieved one by one. Fig. 12 shows the part retrieval results. Theweighting matrix, labelled as 1 in this figure, is generated for productretrieval in part level. In this matrix, each row represents the detailedweighting factors for one part after the weight distribution. Theseweighting factors obtained by using the proposed weight distribu-tion approach will be involved to calculate the similarity degree bythe proposed weighted distance-based function. The monitorwindow labelled as 2 has two lists. The un-retrieved parts are listedin the ‘‘Part List’’, while the retrieved part is added to the ‘‘RetrievedList’’ when its retrieval completes. Users can monitor the progress ofpart retrieval by checking the status of these two lists. As each partmay have more than one configuration, the configuration names ofparts are also retrieved. The bottom window of this GUI showsdetailed information of the retrieved parts, i.e. the part type, the partname, the part path, and the selected configuration name.

Fig. 13 shows the design configuration of the chute system withthe retrieved parts of each module. The design module A, B, and C

denotes the chute module, the brush module, and the frame module,respectively. Each design module is assembled by its retrieved mainparts. The configuration of the required chute system composed bythese modules is shown in the right of this figure.

Compared with designing a chute system from scratch,modifying on previous designs can reduce design time sharply.

The time spent on designing a new system is from 1 to 2 monthswhich depends on the complexity of system (data is from a NZcompany). On the contrary, our method is capable of identifyingthe suitable product configuration and retrieving previous designsfor modification in a short time. The proposed MGPM can enablesOKP companies to manage a large number of product variants in asingle product model. It provides an adequate way to organize OKPproduct data, thus, to enhance the efficiency of case retrieval inOKP. The speed of case indexing and retrieval is improved, whilethe quality of retrieval is ensured by the proposed case retrievaland evaluation process. The WDM approach enables the elicitationand quantification of CPs by mapping at different levels of theproduct configuration. It makes case retrieval more reliable onsatisfying both objective and subjective CRs.

4. Conclusion and future work

This paper presented a new process of case retrieval andevaluation for dealing with the OKP issues. In this process, thecustomer requirements (CRs) are fully interpreted and elicited tofacilitate case retrieval in OKP product design. A proper productconfiguration can be identified by analysing the functionalrequirements (FRs) and the design parameters (DPs). In addition,for the first time, the subjective customer preferences (CPs) arecharacterized and quantified for case evaluation to maximizecustomer satisfactions. The modularized generic product model(MGPM) is proposed for managing customized product data andknowledge to facilitate OKP case retrieval. The MGPM enhancesthe efficiency of case indexing and retrieval. At last, the integrationof the proposed weight distribution model (WDM) and the productconfiguration model (PCM) allows case retrieval in different levelsof the product architecture. The proposed methods and techniquescan deal with the issues (discussed in Section 1.4) in OKP productdesign and case retrieval. A case study on the fruit chute systemwas carried out to demonstrate this.

For future work, the first area is to further develop the proposedMGPM. The MGPM requires the modularization of previousproducts/designs in multi views such as the functional view, thephysical structural view (3D models), and so on. Currently, the

B.M. Li, S.Q. Xie / Computers in Industry 64 (2013) 720–731 731

modularization of product functions and configurations can onlybe completed by designers. In future, an automated/semi-automated way should be developed to accelerate the speed ofproduct modularization. The second research area is to furtheridentify the estimation equations for each functional module/components. The CPs can be other kinds of preferences such asproduct performance specifications. The precision of this approachcan be improved by enhancing the precision of estimationequation, which can be tested and further developed in practice.A set of identified estimation equations should be documented andindexed. The third area of work is to continuously explore thepossibility of extending the proposed case retrieval approach tomore complex products and systems. The constraints dependen-cies among parts, modules, products need to be analysed forfurther case adaptation.

Acknowledgements

The authors would like to acknowledge the support of theInternational Investment Opportunities Fund (IIOF) from theFoundation for Research, Science and Technology (FRST) of NewZealand (Contract number: UOAX0723). We thank Mr. StevenClouston and Mr. Nigel Beach for their assistance.

References

[1] S.Q. Xie, Y.L. Tu, Rapid Development of One-of-a-kind Products: Strategies, Algo-rithms and Tools, Springer, UK, 2010, published in December, 449 pages, 115 illus.1st Edition.

[2] W. Li, P. Li, Y. Rong, Case-based agile fixture design, Journal of Materials ProcessingTechnology 128 (1–3) (2002) 7–18.

[3] B.M. Li, S.Q. Xie, X. Xu, Recent development of knowledge-based systems, meth-ods and tools for one-of-a-kind production, Knowledge-Based Systems 24 (7)(2011) 1108–1119.

[4] D.M.R Lopez, D. McSherry, D. Bridge, Retrieval, reuse, revision and retention incase-based reasoning, Knowledge Engineering Review 20 (3) (2006) 215–240.

[5] I. Boyle, CAFixD: A Case-based Reasoning Method for Fixture Design, WorcesterPolytechnic Institute, 2006.

[6] Y. Nie, G.F. Yin, X.F. Zhao, H. Fang, Y. Yin, Rapid locking assembly variant designbased on product configuration model and CBR, Applied Mechanics and Materials48–49 (2011) 868–872.

[7] W. Yan, Q. Gao, Z. Liu, S. Zhang, Y. Hu, A novel discovery technique on attributeweight of engine CBR design system, Advanced Materials Research 97–101 (2010)3714–3717.

[8] C.-Y. Tsai, C.-C. Chiu, A case-based reasoning system for PCB principal processparameter identification, Expert Systems with Applications 32 (4) (2007)1183–1193.

[9] S.B. Tor, G.A. Britton, W.Y. Zhang, Indexing retrieval in metal stamping die designusing case-based reasoning, Journal of Computing and Information Science inEngineering 3 (2003) 353–362.

[10] M.M. Tseng, R.J. Jiao, C. Wang, Design for mass personalization, CIRP Annals –Manufacturing Technology 59 (1) (2010) 175–178.

[11] S.-S. Xia, L.-Y. Wang, Customer requirements mapping method based on associa-tion rules mining for mass customisation, International Journal of ComputerApplications in Technology 37 (3) (2010) 198–203.

[12] Y. Wang, M.M. Tseng, Integrating comprehensive customer requirements intoproduct design, CIRP Annals – Manufacturing Technology 60 (1) (2011) 175–178.

[13] W. Yan, L.P. Khoo, C.-H. Chen, A QFD-enabled product conceptualisation approachvia design knowledge hierarchy and RCE neural network, Knowledge-BasedSystems 18 (6) (2005) 279–293.

[14] T.L. Saaty, Analytic hierarchy process, in: Encyclopedia of Biostatistics, John Wiley& Sons, Ltd., 2005.

[15] J. Liu, Y. Wang, Fuzzy analytical hierarchy process-based assembly unit partitionfor complex products, in: ASME Conference Proceedings 2007 (48078), 2007, pp.121–128.

[16] W.-C. Chen, S.-S. Tseng, L.-P. Chang, T.-P. Hong, M.-F. Jiang, A parallelized indexingmethod for large-scale case-based reasoning, Expert Systems with Applications23 (2) (2002) 95–102.

[17] B.-S. Yang, S. Kwon Jeong, Y.-M. Oh, A.C.C. Tan, Case-based reasoning system withPetri nets for induction motor fault diagnosis, Expert Systems with Applications27 (2) (2004) 301–311.

[18] H. Wang, Y. Rong, Case based reasoning method for computer aided weldingfixture design, Computer-Aided Design 40 (12) (2008) 1121–1132.

[19] X.-t. Zhang, X. Hou, T.-G. Jin, A novel method for design cases indexing andretrieval, in: IEEE/ASME International Conference on Advanced Intelligent Mecha-tronics, 2–5 July, (2008), pp. 1093–1097.

[20] R.Y.K. Fung, K. Popplewell, J. Xie, An intelligent hybrid system for customerrequirements analysis and product attribute targets determination, InternationalJournal of Production Research 36 (1) (1998) 13–34.

[21] W.L. Chen, S.Q. Xie, F.F. Zeng, B.M. Li, A new process knowledge representationapproach using parameter flow chart, Computers in Industry 62 (1) (2011) 9–22.

[22] M.-C. Lin, C.-C. Wang, M.-S. Chen, C.A. Chang, A.H.P. Using, TOPSIS approaches incustomer-driven product design process, Computers in Industry 59 (1) (2008)17–31.

[23] A. Briere-Cote, L. Rivest, A. Desrochers, Adaptive generic product structuremodelling for design reuse in engineer-to-order products, Computers in Industry61 (1) (2010) 53–65.

[24] S. Naciri, N. Cheikhrouhou, M. Pouly, J.-C. Binggeli, R. Glardon, ERP data sharingframework using the generic product model (GPM), Expert Systems with Appli-cations 38 (2) (2011) 1203–1212.

[25] S.Q. Xie, P.L. Tu, D. Aitchison, R. Dunlop, Z.D. Zhou, A www-based integratedproduct development platform for sheet metal parts intelligent concurrent designand manufacturing, International Journal of Production Research 39 (17) (2001)3829–3852.

[26] Y.-P. Luh, C.-H. Chu, C.-C. Pan, Data management of green product developmentwith generic modularized product architecture, Computers in Industry 61 (3)(2010) 223–234.

[27] F. Erens, H.C. Wortman, Generic Product Modeling for Mass Customization, AnnArbor, MI, 1996.

[28] Q. Liu, J. Xi, Case-based parametric design system for test turntable, ExpertSystems with Applications 38 (6) (2011) 6508–6516.

[29] S. Tao, Z. Huang, B. Zuo, Y. Peng, W. Kang, Partial retrieval of CAD models based onthe gradient flows in lie group, Pattern Recognition 45 (4) (2012) 1721–1738.

[30] D. Wang, G. Yan, Y. Lei, J. Zhang, A retrieval algorithm of sheet metal partsbased on relationships of features, Chinese Journal of Aeronautics 25 (3) (2012)453–472.

[31] J. Bai, S. Gao, W. Tang, Y. Liu, S. Guo, Design reuse oriented partial retrieval of CADmodels, Computer-Aided Design 42 (12) (2010) 1069–1084.

[32] M. Li, Y.F. Zhang, J.Y.H. Fuh, Retrieving reusable 3D CAD models using knowledge-driven dependency graph partitioning, Computer Aided Design & Applications 7(3) (2010) 417–430.

[33] M. Zhang, G.X. Li, J.Z. Gong, B.Z. Wu, Technology of product configuration designbased on ordered-tree, Computer Integrated Manufacturing Systems 16 (11)(2010) 2333–2340.

[34] T.J. van Beek, M.S. Erden, T. Tomiyama, Modular design of mechatronic systemswith function modeling, Mechatronics 20 (8) (2010) 850–863.

[35] Ulrich, T. Karl, D. Eppinger Steven, Anita Goyal, Product design and development,2, McGraw-Hill, 2011.

[36] S.Q. Xie, Y. Tu, Rapid one-of-a-kind product development, The InternationalJournal of Advanced Manufacturing Technology 27 (5) (2006) 421–430.

[37] S.Q. Xie, Y. Tu, et al., Rapid one-of-a-kind product development via the Internet: aliterature review of the state-of-the-art and a proposed platform, InternationalJournal of Production Research 41 (18) (2003) 4257–4298.

[38] S.Q. Xie, X. Xu, et al., A reconfigurable platform in support of one-of-a-kindproduct development, International Journal of Production Research 43 (9)(2005) 1889–1910.

[39] S.Q. Xie, A decision support system for rapid one-of-a-kind product development,The International Journal of Advanced Manufacturing Technology 28 (7–8) (2006)643–652.

Bomiao Li received her BE (2006) and ME (2009) fromHuazhong University of Science and Technology(HUST), China. She also received a BSc degree (2006)from Wuhan University, China. Currently she is a PhDcandidate in mechatronics at the University of Auck-land and doing research in the field of One-of-a-KindProduction development technologies, methods andtools.

Prof. Shane (S.Q.) Xie received his Ph.D degrees fromHuazhong University of Science and Technology(China), and University of Canterbury (New Zealand).He is Chair Professor in Mechatronics engineering atUniversity of Auckland, New Zealand. His researchinterests include intelligent mechatronics systems,vision techniques and applications, smart sensors andactuators, Bio-mechatronics and Biomedical robotics.He has more than 20 years of teaching and researchexperience in mechatronics and robotics. He is theeditor of two international journals, and is an editorialboard member and scientific advisory member formany international journals and conferences. He haspublished more than 200 papers in refereed interna-tional journals and conferences.