ejemplo aplicaciÓn qfd

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International Technology and Innovation Conference 2006

A QFD-ENABLED DESIGN FORMANUFACTURINGAPPROACH VIADESIGN KNOWLEDGE HIERARCHYAND RCE NETWORK

CHANG D. F., YAN W., HUANGY. F.,MIW. J., HUANG S. J.Logistics Engineering School, Shanghai Maritime University,1550 Pu Dong Da Dao, Shanghai 200135, China

Keywords: Design for manufacturing, quality functiondeployment, laddering technique, design knowledgehierarchy, restricted coulomb energy neural network.AbstractIn recent years, owing to the rapid changing customerneeds and short product life cycle than ever before,employing more effective and flexible approaches for newproduct development (NPD), product concept developmentin particular, has become an imperative for a successfulproduct. In this paper, an approach that attempts toimprove conventional quality function deployment (QFD)technique in terms of effective design for manufacturing(DFM) in product concept development is proposed anddescribed. For this purpose, a QFD-enabled DFM systemwas established. It consists of three cohesively-interactingmodules, namely, DFM knowledge elicitation moduleusing laddering technique; DFM knowledge representationmodule using design knowledge hierarchy (DKH); andDFM knowledge organisat ion module using restrictedCoulomb energy (RCE) neural network. A case study onwood golf club design was used to illustrate theperformance of the proposed approach. The details of thevalidation are discussed. From the case study, theprototype QFD-enabled DFM system has demonstrated itseffectiveness in DFM knowledge acquisition,representation and organisation at the early stage ofNPD.1 IntroductionIt is a well known fact that product conceptualisation plays acrucial role in new product development (NPD) to reduce thenumber of design iterations. Moreover, to develop asuccessful product in today's competitive and globalisedenvironment, customer requirements need to be carefullyconsidered in product conceptualisation [1]. For this purpose,quality function deployment (QFD) has been widely studiedand applied to better understand and utilise customer needs inNPD, such as robust design, business planning, and problemsolving [2]. As such, customer requirements elicitation

becomes the starting point of employing QFD technique forproduct planning and conceptualisation.In the previous work, ReVelle et al. [3] developed aframework, which is an extension of QFD, for Taguchi'srobust design. Althoughmuch research has been conducted inthe domain of customer involvement in NPD, the problempertaining to how to genuinely acquire customer requirementsand subsequently use those requirements as inputs to QFDhave not been well addressed. In essence, QFD is a practicalmethod for transforming customer attributes into designspecifications in the early stage ofNPD. In order to develop aproduct conceptual framework, Houvila and Seren [4] appliedboth QFD technique and design structure matrix inestablishing an approach that assists in understandingcustomer needs and planning for design process at the earlystage of product conceptualisation. However, theabovementioned approach, which is dependent much on thedecisions made by domain experts and the reliability of thecustomer attributes, are difficult to be assessed accurately.Basically, a complete QFD process provides a traceable pathto bring the overall customer concerns into the productdevelopment process from conceptual design through tomanufacturing [5]. Accordingly, design for manufacturing(DFM) methodology can be used to bridge both design andmanufacturing paradigms [6,7]. Although QFD technique hasbeen widely adopted to derive product definitions in thedevelopment of a new product, it is not yet integrated withsuch methods as DFM. This is because the elicitation,representation and organisation of design knowledge have notbeen well addressed under a unified framework. In this regard,the following obstacles encountered in managing customers'and designers' knowledge. Requirements elicitation. Customer and designerrequirements acquisition under a unified yet simpleframework is scarce.

Requirements transformation. Differences inlinguistic attributes affect the transformation ofinformation from customers to designers due todifferent perspectives. Configuration mapping. The relationships betweencustomer needs, designer requirements and productconcepts are often not clearly defined or available atthe early stage of design. Decision-making. The interrelationships amongrequirements are usually expressed in abstract, fuzzy

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Section 1 AdvancedManufacturing Technology

or conceptual terms, which cause difficulty inmaking design decisions. Concept specifying. The transformation of customerdesigner requirements and!or product concepts into aspecific product design needs to be addressed.In order to deal with these obstacles, from a design for

manufacturing viewpoint, the proposed approach aims atestablishing a QFD-enabled product conceptualisationsystem for organisations to meet the demand of aneffective strategy for product concept development. Theproposed prototype system elicits DFM knowledge usingthe laddering technique, represents DFM knowledge basedon a so-called design knowledge hierarchy (DKH) andorganises DFM knowledge via restricted Coulomb energy(RCE) neural network. It suggests a method of makingdesign decisions based on a QFD-enabled productconceptualising approach, i.e., a strategy for transferringcustomer information into a specific product concept. Acase study on wood golf club design has been used toillustrate and validate the system.2 System frameworkIn general, QFD is a set of matrices that relate inputs (e.g.customer voices) to outputs (e.g. design specifications). Inorder to solicit design knowledge from broader knowledgesources, it is imperative to elicit both customers' anddesigners' knowledge in formulating the 'house-of-quality'(HoQ). On the basis of the HoQ, a prototype QFD-enabledDFM system is established in this study. Figure 1 shows aframework of the proposed prototype system. From a DFMknowledge-handling viewpoint, the proposed system consistsof three correlated modules, namely, a DFM knowledgeelicitation (DKE) module, a DFM knowledge representation(DKR) module and a DFM knowledge organisation (DKO)module, as follows.(i) DFM knowledge elicitation (DKE) moduleA number of complex human behaviours, such as perceptions,motivations, attitudes, and personality, influence the way inwhich customers or designers organise and interpret a productand its DFM issues. In this respect, a simple yet effectivetechnique to acquire the customers' and designers' knowledgeis highly desirable in forming a product concept. Based onthis understanding, a well-established knowledge acquisitiontechnique, known as the laddering technique [8], presents alogical alternative for design knowledge acquisition inproduct conceptualisation.(ii) DFM knowledge representation (DKR) moduleA simplified approach is required to represent the customers'or designers' knowledge acquired in different abstractionlevels within a unified knowledge representation scheme. Asa result, a so-called design knowledge hierarchy (DKH) hasbeen proposed and implemented to handle design knowledgeextracted from multiple design knowledge carriers such ascustomers and designers viz. customer attributes hierarchy(CAH) and functional attributes hierarchy (FAH),

respectively [9,10]. Furthermore, it can be used to quantifythe DFM properties using such information as customerratings in the heuristic design evaluation.(iii) DFM knowledge organisation (DKO) moduleDesign knowledge reasoning and design decision-makingplaya core role in narrowing down the design solution ordecision space based on DFM criteria and constraints. Thus,product concept becomes quite qualitative and uncertainwhen represented by a hierarchical structure alone. In thisrespect, a restricted Coulomb energy (RCE) neural network[11] is used for organising design knowledge quantitativelybased on QFD technique.

DKOModuie

Figure 1: framework of the QFD-enabled DFM system.3 DFM knowledge elicitation using ladderingtechniqueLaddering is a structured questioning methodology derivedfrom Kelly's repertory grid technique [12]. In recent years, ithas also been applied and improved with increasing frequencyin the field of knowledge and requirements acquisition forproduct conceptualisation [9,10]. Laddering provides astructure for the elicitation of information using a 'facet' ,which is a convenient way to describe individual hierarchyand decomposition requirements. The procedures of theladdering technique, originally presented by Rugg andMcGeorge [8], is summarised as follows.Step 1: Selectinglfaceting a seed item. An interviewer firstselects a seed item, which is a point within the domain inquestion, from any level within the hierarchy.Step 2: Preparing/phrasing the probes. The interviewer usesprobing questions to move around the structure embeddingthe seed item.Step 3: Directing/levelling the semantics. Although ladderingproceeds simply and recursively, different prompts arerecommended to alter the direction once laddering is notpossible to go any further in a particular direction.Step 4: Decomposing/classing the explanations. As the depthof explanations can be treated as an indication of requirement

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International Technology and InnovationConference 2006

complexity, also known as elucidatory depth, explanations arethen decomposed recursively until terms such as classes,attributes and entities have bottomed out.Step 5: Recording/coding the sessions. Several codingmethods are available for laddering; including paper record,graphic representation and pseudo-production rule.Step 6: Analysing/post-processing the results. This enablesthe elicitors to gain insights into the results of laddering.Quantitative analysis can be employed to post-process theresults obtained.

specifications, and each sub-specification contains severalalternative values (Figure 3). Typical design alternatives canbe selected from different combinations of alternative valuesusing the FAH to form a specific product family. In the samemanner, the customer-perceived product platform can beeffectively formulated by another hierarchical structureknown as the customer attributes hierarchy (CAH) as shownin Figure 3. In this multi-level taxonomy, each imposedconstruct can be decomposed into several superordinateconstructs, and each superordinate construct contains severalverbatim constructs.4 DFM knowledge representation using designknowledge hierarchy DKH

5 DFM knowledge organization using RCEneural network

I Customer-perceived Product PlalformI '----......- - - . . . ,IIIIIIIII!IL ~

Prototype

Attemative

,Property,

Sub-property,III~

FAH

Figure 3: relationship between CAH and FAH.

IIiIiIII!I\'-

The imposed constructs elicited from the CAH can be treatedas one dimension of inputs for the QFD relationship matrix,while the design specifications solicited from the FAH can beregarded as the other dimension of inputs. Subsequently, thecorrelation between imposed constructs and designspecifications is further evaluated to generate technical targets.Neural network has been proven to be one of the mosteffective artificial intelligence (AI) techniques for engineeringapplications, of which NPD is an important applicationdomain. In this study, the restricted Coulomb energy (RCE)neural network [11] is adopted for the purpose of DFMknowledge organisation. Compared with other neuralnetworks, it has the following advantages: (1) guaranteedconvergence of thresholds in hidden units; (2) usually 3 to 4epochs for fast training; (3) pre-determining hidden unitnumber not required; (4) Class handling even with disjointregions; (5) classification results from supervised strategybeing controlled more accurately; and (6) dynamic new classlearning despite of re-training of entire sample set.

Figure 2: representation ofDKH.

Product concept development is a complicated process. Ahierarchical structure is helpful in organising the multidisciplinary knowledge. Accordingly, in this work, the DFMknowledge hierarchy (DKH) (Figure 2), which possesses ageneric hierarchical structure to represent the DFMknowledge extracted from multiple design knowledge carrierssuch as customers and designers (i.e. customer attributeshierarchy (CAH) and functional attributes hierarchy (FAH),respectively), is adopted [9,10].

Inheritance

~ u n c t i o n a l Linkage - - ~ e l a t i o n a l Linkage

The DKH has two integral domains, namely the functionaland relational components.(i) The functional domain. Basically, the DKH is a four-leveltop-down knowledge-carrier-oriented architecture for therepresentation of product concepts.(ii) The relational domain. The entities of the DKH interrelateand interact with each other in both directions via the fivetypes of relations, i.e., the abstraction-instantiation pair, theinheritance-polymorphism pair, and the semantic relationoccurs in the DKH (e.g. 'part-of and 'is-a' relations).The FAH, which accesses the designers' knowledgeregistered in the DKH, comes with a four-level top-downdesigner-directed architecture for the decomposition of aspecific product concept. In this multi-level taxonomy, eachdesign specification can be decomposed into several sub-

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5.1 Algorithm of the restricted Coulomb energy (RCE)neural networkThe RCE network generally consists of three layers, viz. the 2.input layer, the hidden layer and the output layer. The inputlayer that made up of sensory units from input samples offeature vector is fully interconnected with the hidden layer,which provides a set of functions of the input patterns. Thehidden layer is partially interconnected with the output layer,which offers the response of the network to the activationpatterns, e.g. output patterns of weak, moderate and stronglevel. Figure 4 shows the architecture of the RCE network.Mathematically, the activation function of jth hidden unit isgiven by [11]:

inactive. Note that a pre-defined mInImum size ofinfluence region rmin is set for the hidden unit to beshrunk at most.If x2 happens to belong to the same class as Xl but doesnot cause the output to fire. A new hidden unit isallocated with centre at P2 = r and threshold rmax' Theoutput Z2 of this unit is connected to the output units.Now, if the input pattern causes no output units to fire, anew hidden unit centred at the current input pattern isallocated with a threshold r = minermax, max(dmin, rmin)),where dmin is the distance from this new centre to thenearest centre of a hidden unit representing any classdifferent from that of the current input pattern.

Input Samples

HiddenLayer

Figure 4: architecture of the RCE network.

Zj(X) = f ( ~ ) = f[ rj - D(Pjl x)] (1)where Pi is a parameter vector called centre, rj is athreshold andD is the pre-defined distance between Pi andx, e.g. Euclidean distance. The jth hidden unit in the RCEnetwork defines the unit's influence region with location atpj and size of rj. Here, f is the threshold activation functionand is given by

f ( ) = {1 (fire) i f 0 (2); 0 (inactive) otherwiseThe RCE network training involves two mechanisms, viz.unit commitment and modification of hidden unit threshold.Initially, a random sample pattern X l is selected from thetraining set. The allocated hidden-unit centre, PI, whichprojects to its output unit Zl representing the class of X l, isset equal to X l. Its threshold r l is set equal to a pre-definedmaximum size of influence region rmax Next, a secondrandom example x 2 is fed into the current network. Twosituations are possible:1. If x2 causes the output unit to fire, and x 2 belongs to theclass that is represented by this unit, training continueswith a new input. This proceeds on by reducing thethresholds of all active hidden units associated withclasses other than the correct one, until they become

The training process continues until no new units areallocated and the size of the influence region of all hiddenunits converges. After training, the classification proceedsusing the trained network.5.2 Specifying the RCE network for design knowledgeorganisationThe customer importance rating assigned to the imposedconstructs of the CAH (an MxN matrix with M-dimensionalinput features and N-dimensional respondents for each designspecification of the FAH) is used as an input to an RCEnetwork for QFD-enabled DFM knowledge organisation.Each design specification is represented by an M xN trainingmatrix extracted from customer importance ratings forimposed constructs. The classification strategy regardingoutput activation and related decision-making are described inTable 1.

Possible State 0 State 1 State 2 DecisionSituation (weak (Moderate (Strong -Making

Level) Level) Level)1 0 0 0 U2 1 0 0 I3 0 1 0 I4 0 0 1 I5 1 1 0 U6 1 0 1 U7 0 1 1 U8 1 1 1 U

Table 1: classification strategy regarding output activation ofthe RCE network (0 stands for inactive and 1 stands for fire,U denotes Unidentified and I denotes Identified).If 'Identified' situation is detected, three possibilities for RCEnetwork classification exist. Amongst them, a weakcorrelation between a pair of input and output is denoted asState O. Similarly, a moderate correlation and a strongcorrelation can be denoted as States 1 and 2, respectively. Onthe other hand, there exist 'Unidentified' and 'Uncertain'situations corresponding to different output activation asshown in Table 1.

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International Technology and Innovation Conference 2006

6 A case studyThis case study involved the design of a wood golf club. Inapplying the laddering technique, twenty (20) customers andfour (4) designers attended the interview for establishing theCAH and FAH, respectively. In this work, the RCE networkwas employed after the respondents completed customerratings of imposed constructs for each design specification.

Imposed Superordinate Exampleo!Construct Construct Verbatim Construct

I. Mfg Cust Segment Likes the designMarket Design Alter Provide market infoII. Mfg Pricing Strategy Priced reasonablyCost Cost Estimation Low cost possibleIII. Mfg Standard Issue Follow design standardPrinciple Part Standard Use standard partsIV. Mfg Mfg Metric Is easy to manufacturePrinciple Assembly Issue Is easy of assemblyV.Mfg Prod Material Use newmaterialProcess Durability Issue Can be used longMechanics Issue Possess mechanismUsability Issue Is easy to useVI. Mfg Design Style Provide fashion stylesCustomisation ProdAppearance Have good appearanceTailored Spec Provide dimensionsProduct Fitting Suit specific customer

Table 2: properties of the CAH.Design Sub- Alternative

Specification specification ValueI. Head General Stainless SteellForgedMaterial Specific Ti AlloylForged, Ti Alloy/(Face/Body) Cast, Maraging SteellForgedII. Length Short 112 cmLong 115 cmIII. Head Standard 12/57, 11/57Angle Specific 10/56, 10/55,(Loft/Lie) 9/55IV. Total Low 250g,260gWeight Medium 270g,280gHigh 290g,300gV. Head Low 260cm3Volume Medium 270cm3,280cm3

High 290cm3, 300cm3VI. Shaft General CarbonMaterial Specific Light Carbon, FibreglassVII. Flex Reflex R,SRStiff S,X,RSVIII. Low $500,$600Estimated Medium $700,$800Price High $900, $1,000

classification scheme of the RCE neural network is dependenton the multicultural factors elicited from the output patternsfor the design specifications, which are organised in the formof input matrices and gathered from groups of respondentshaving different gender, age and skill. The results obtainedare organised according to the three multicultural factors, viz.skill, gender and age, in Tables 4, 5 and 6, respectively.It was observed that some kind of output patterns could beidentified under each gender, age and skill grouping. EitherPatterns 0 and 1 or Patterns 1 and 2 were instantiated for mostdesign specifications. 'Uncertain pattern' and 'Unidentified',though not significantly prominent, were also observed in thedistribution due to the output activation of the RCE networkclassification. Some form of similarities (commonality ofdistribution) can be observed between two different gender,age and skill groups. For example, male and female golfersemphasised Design Specification 'Flex', because the majorityof output patterns was linked to State 2 (high level). Hence,differences (adverse correlation) can still be spotted asdifferent groups possessed different distribution patterns forsome design specifications.

It was found from Table 4 that, in different gendergroups, male players concerned more with DesignSpecification 'Flex' while female golfers consideredmore about Design Specifications 'Length' as wellas 'Flex'. It was detected from Table 5 that respondents over35-year-old focused on Design Specification'Estimated Price' much more than respondentsbelow 35-year-old did, as 34 out of 40 responses

from the former group were linked to State 2 (highlevel) while only 5 out of 40 for the latter group. Asfor Design Specifications 'Head Material', 'TotalWeight' , 'Shaft Material' and 'Flex', it is obviousthat the below 35-year-old group paid much moreattention to them than its counterpart did. It was observed from Table 6 that beginner golfersconcentrated more on Design Specifications 'Flex'and 'Estimated Price' whereas better amateur golferspaid more attention to the other design specifications.DS Male Female

Out7ut Pattern Out,out Pattern0 1 2 UC UI 0 1 2 UC UI

I 9 28 1 2 0 24 13 0 2 1II 0 6 31 2 1 0 5 34 0 1III 22 15 0 1 2 12 19 6 3 0IV 0 21 16 3 0 0 11 27 1 1V 30 8 0 0 2 15 23 2 0 0VI 19 17 2 1 1 25 13 0 2 0VII 0 9 29 2 0 0 7 32 1 0VIII 18 11 7 2 2 8 20 9 1 2

Table 3: properties of the FAH.The graded imposed constructs were then organised into afeature vector and used as inputs to the RCE network fortraining and classification. More specifically, the

Table 4: statistical results based onDS 53 5

ut Patterno 2 UC UI

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Table 5: statistical results based on age.DS Beginner Better Amateur

Outout Pattern Output Pattern0 1 2 UC UI 0 1 2 UC UI


Table 6: statistical results based on skill.7 Concluding remarksAn attempt has been made to study the possibility ofimproving conventional QFD technique in terms of effectiveDFM in product concept development. To realise this, aQFD-enabled DFM system was established. It consists ofthree cohesively-interacting modules, viz. DKE module usingladdering technique; DK R module using DKH; and DKOmodule using RCE neural network. The work has yielded thefollowing.

The laddering technique has been proven to bepromising in systematically acquiring DKMknowledge from such knowledge carriers as customersand designers and subsequently using the knowledgeas the inputs to the HoQ matrix.

A so-called DKH has been developed as a logical andnovel knowledge representation scheme associatedwith laddering technique. Based on the DKH, bothCAH and FAH were established for customers ' anddesigners' knowledge representation, respectively.

A novel classification strategy based on the RCEnetwork has been proposed to analyse multiculturalcustomer factors (e.g. gender and age), i.e.identification of output patterns with respect to diversemulticultural customer groups, and evaluate therelationship between the customers ' and designers 'knowledge.

A case study on wood golf club design was used to illustratethe performance of the proposed approach. From the casestudy, the proposed prototype QFD-enabled DKM system hasdemonstrated its effectiveness in DK M knowledge acquisition,representation and organisation at the early stage of NPD. It isenvisaged that with the comprehensiveness of DK M

knowledge acquired from both designs and customers, morereasonable product concepts can be gleaned. As a result,organisations can gain a competitive edge in NPD.

AcknowledgementsThis research work is sponsored by Shanghai EducationCommittee Research Projects (Project Numbers 04GZ65 and05FZ28).References[1] J. A. Harding, K. Popplewell, R. Y. K. Fung, A. R.

Omar, "A n intelligent information framework relatingcustomer requirements and product characteristics",Computers in Industry, 44, 51-65,2001.

[2] G. Burchill, C. H. Fine, "Time versus market orientationin product concept development: Empirically-basedtheory generation", Management Science, 43(4), 465478,1997.

[3] 1. B. ReVelle, 1. W. Moran, C. A. Cox, The QFDHandbook, John Wiley & Sons, New York, (1998).[4] P. Houvila, K.J. Ceren, "Customer-oriented design

methods for construction proj ects", Journal ofEngineering Design, 9(3), 225-238, 1998.

[5] R.Y. K. Fung, K. Popplewell, 1. Xie, "A n intelligenthybrid system for customer requirements analysis andproduct attribute targets determination", InternationalJournal ofProduction Research, 36(1), 13-34,1998.

[6] M.M. Tseng, M.M. 1.X. Jiao. "Design for masscustomization", Annals of the CIRP, 45(1), 153-156,1996.

[7] L. Alting, "Life cycle engineering and design", Annalsofthe CIRP, 44(2), 569-580,1995.

[8] G. Rugg, P. McGeorge, "Ladder ing" , Expert Systems,12(4), 279-291, 1995.

[9] W. Yan, C. H. Chen, L. P. Khoo, "A radial basisfunction neural network multicultural factors evaluationengine for product concept development", ExpertSystems, 18(5),219-232,2001.

[10] L.P. Khoo, C.H. Chen, W. Yan, "A n investigation on aprototype customer-oriented information system forproduct concept development", Computers in Industry,49(2), 157-174, (2002).

[11] D. L. Reilly, The RCE Neural Network, IndustrialElectronics Handbook, CRC Press, Ne w York, 1983.[12] G. A. Kelly, "The Psychology ofPersonal Constructs",

W. W. Norton, Ne w York, 1955.

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