selection strategies materials processes ashby brechet cebon salvo

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Materials and Design 25 (2004) 51–67 0261-3069/04/$ - see front matter 2003 Elsevier Ltd. All rights reserved. doi:10.1016/S0261-3069(03)00159-6 Selection strategies for materials and processes M.F. Ashby , Y.J.M. Brechet *, D. Cebon , L. Salvo a b, a c ´ Engineering Department, University of Cambridge, Trumpington Street, Cambridge CB2 1PZ, UK a L.T.P.C.M., Domaine Universitaire de Grenoble, BP75, 1130, rue de la Piscine, Saint Martin d’Heres Cedex 38402, France b G.P.M.2., Domaine Universitaire de Grenoble, BP75, Saint Martin d’Heres Cedex, France c Received 18 February 2003; accepted 21 July 2003 Abstract Engineering design draws on tens of thousands of materials and on many hundreds of processes to shape, join and finish them. One aspect of optimized design of a product or system is that of selecting, from this vast menu, the materials and processes that best meet the needs of the design, maximizing its performance and minimizing its cost. The problem, still incompletely solved, is that of matching material and process attributes to design requirements. Some of these attributes can be expressed as numbers, like density or thermal conductivity; some are Boolean, such as the ability to be recycled; some, like resistance to corrosion, can be expressed only as a ranking (poor, adequate, good, for instance); and some can only be captured in text and images. Achieving the match with design requirements involves four basic steps. (1) A method for translating design requirements into a specification for material and process. (2) A procedure for screening out those that cannot meet the specification, leaving a subset of the original menu. (3) A scheme for ranking the surviving materials and process, identifying those that have the greatest potential. (4) A way of searching for supporting information about the top-ranked candidates, giving as much background information about their strengths, weaknesses, history of use and future potential as possible. In this paper we review the strategies that have evolved to deal with this problem, the progress that has been made and the challenges that remain. 2003 Elsevier Ltd. All rights reserved. Keywords: Selection strategies; Engineering design; Materials and process selection softwares; Expertise management 1. Introduction Life is full of difficult decisions and none is fuller than that of the designer. Among them are the decisions concerning the choice of materials and processes. There are—it is estimated—between 40 000 and 80 000 mate- rials and at least 1000 different ways to process them. The designer needs information about all of these if he is to optimize the choice. To put these numbers into perspective, compare them with the vocabulary of an ordinary European: approximately 5000 words. There is a self-evident need here for information-management systems. In recent years, the field of computer aided materials and process selection in mechanical design has evolved from a pedagogical tool into systems closer to the needs of design engineers. This evolution has been *Corresponding author. Tel.: q33-47-682-6610; fax: q33-47-682- 6644. E-mail address: [email protected] (Y.J.M. Brechet). ´ both in the development of structured databases for materials and processes and in the improvement of systematic methods to compare materials and processes for multi-criteria selection. As a consequence of this evolution, a number of new questions have arisen, suggesting new directions for research. The aim of this paper is to provide a review of the situation. Section 2 outlines the needs for materials and process selection at various steps of the design and the nature of information that these require. In Section 3 we present the possible selection strategies: the ‘free search’, the ‘questionnaire based’ and the ‘analogy’. The choice of the most appropriate strategy for a given selection problem is a key issue in the development of efficient selection guides. The practical application of selection methods requires both the identification of a ‘value function’ and the exploration of the space of possible solutions using optimization methods that depend on the nature of the set of possible solutions (discrete, semi-

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Page 1: Selection Strategies Materials Processes Ashby Brechet Cebon Salvo

Materials and Design 25(2004) 51–67

0261-3069/04/$ - see front matter� 2003 Elsevier Ltd. All rights reserved.doi:10.1016/S0261-3069(03)00159-6

Selection strategies for materials and processes

M.F. Ashby , Y.J.M. Brechet *, D. Cebon , L. Salvoa b, a c´

Engineering Department, University of Cambridge, Trumpington Street, Cambridge CB2 1PZ, UKa

L.T.P.C.M., Domaine Universitaire de Grenoble, BP75, 1130, rue de la Piscine, Saint Martin d’Heres Cedex 38402, Franceb

G.P.M.2., Domaine Universitaire de Grenoble, BP75, Saint Martin d’Heres Cedex, Francec

Received 18 February 2003; accepted 21 July 2003

Abstract

Engineering design draws on tens of thousands of materials and on many hundreds of processes to shape, join and finish them.One aspect of optimized design of a product or system is that of selecting, from this vast menu, the materials and processes thatbest meet the needs of the design, maximizing its performance and minimizing its cost. The problem, still incompletely solved,is that of matching material and process attributes to design requirements. Some of these attributes can be expressed as numbers,like density or thermal conductivity; some are Boolean, such as the ability to be recycled; some, like resistance to corrosion, canbe expressed only as a ranking(poor, adequate, good, for instance); and some can only be captured in text and images. Achievingthe match with design requirements involves four basic steps.(1) A method for translating design requirements into a specificationfor material and process.(2) A procedure for screening out those that cannot meet the specification, leaving a subset of theoriginal menu.(3) A scheme for ranking the surviving materials and process, identifying those that have the greatest potential.(4) A way of searching for supporting information about the top-ranked candidates, giving as much background informationabout their strengths, weaknesses, history of use and future potential as possible. In this paper we review the strategies that haveevolved to deal with this problem, the progress that has been made and the challenges that remain.� 2003 Elsevier Ltd. All rights reserved.

Keywords: Selection strategies; Engineering design; Materials and process selection softwares; Expertise management

1. Introduction

Life is full of difficult decisions and none is fullerthan that of the designer. Among them are the decisionsconcerning the choice of materials and processes. Thereare—it is estimated—between 40 000 and 80 000 mate-rials and at least 1000 different ways to process them.The designer needs information about all of these if heis to optimize the choice. To put these numbers intoperspective, compare them with the vocabulary of anordinary European: approximately 5000 words. There isa self-evident need here for information-managementsystems. In recent years, the field of computer aidedmaterials and process selection in mechanical design hasevolved from a pedagogical tool into systems closer tothe needs of design engineers. This evolution has been

*Corresponding author. Tel.:q33-47-682-6610; fax:q33-47-682-6644.

E-mail address: [email protected](Y.J.M. Brechet).´

both in the development of structured databases formaterials and processes and in the improvement ofsystematic methods to compare materials and processesfor multi-criteria selection. As a consequence of thisevolution, a number of new questions have arisen,suggesting new directions for research. The aim of thispaper is to provide a review of the situation.

Section 2 outlines the needs for materials and processselection at various steps of the design and the natureof information that these require. In Section 3 we presentthe possible selection strategies: the ‘free search’, the‘questionnaire based’ and the ‘analogy’. The choice ofthe most appropriate strategy for a given selectionproblem is a key issue in the development of efficientselection guides. The practical application of selectionmethods requires both the identification of a ‘valuefunction’ and the exploration of the space of possiblesolutions using optimization methods that depend on thenature of the set of possible solutions(discrete, semi-

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Fig. 1. The design flow-chart. Materials and process information isrequired at every step—breadth at the top, detail at the bottom.

Fig. 2. Data for materials and processes takes a spectrum of forms, ranging from numeric data for standard properties(like density) to experiencederived from past applications. One challenge in constructing selection tools is that of making the maximum use of all forms of data.

discrete or continuous). The problems of choosing avalue function and selecting an exploration tool aredetailed in Section 4. The methodology outlined herehas led to number of generic and specific software tools.Section 5 will present examples of these, ranging fromthe most general to the most specialized. A diagnosticprocedure for the development of specialized softwareis outlined. A number of open questions are identifiedin Section 6, suggesting the need for research in both

new methodological approaches and new optimizationtechniques.

2. Needs of the designer and nature of the requiredinformation

Fig. 1 sets the scene. The backbone represents thedesign process, starting with a market need, proceedingthrough the stages of concept generation(the idea),embodiment development(the sketch) and detaileddesign (the working drawings), finally ending in pro-duction. New design starts at the top and proceedsdownwards. Re-design starts at the bottom and loopsupwards before descending.

Materials and process information is necessary forevery stage of Fig. 1—approximate data for all materialsand processes at the top; precise and detailed data forone or a few materials and processes at the bottom. Thenature of this data is captured by the schematic of Fig.2. At the top left sits a nugget of numeric data: valuesfor precisely measurable properties like density, modu-lus, strength and thermal conductivity. Some propertiesare not so easily expressed by numbers: resistance tocorrosion or to wear are examples. They can, instead,be described by a ranking: ‘excellent’, ‘good’, ‘average’,‘poor’, ‘awful’, for instance, or simply A to E. Othersare a matter of yesyno, Boolean, decisions: can thematerial be blow-moulded? Can the process shape alu-minium alloys? Database technology is good at organ-

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izing, storing and manipulating information that can beexpressed in any of these ways, which we will refer toas structured data. It is the stuff of materials handbooksand data sheets and is generally available, in standardformats, for almost all materials.

However, think of all the stuff that is not in datasheets. There are suppliers’ guidelines for design with amaterial; there are case studies of its use in particularmarket sectors; there are analyses of failures caused byits ill-advised deployment; there is, in short, the accu-mulated experience of the used of the material(Fig. 2).Such experience may come from reference sources ormay take the form of accumulated ‘in-house’ experi-ence—failure reports, lab notebooks and the like. It isvaluable information that we ignore at our peril—tomake a mistake once is a misfortune; to make it twiceis gross carelessness. This sort of specific supportinginformation can be indexed by material name, but inany other regard it is not easily structured and existsonly in patchy form, some bits for some materials, otherbits for others. Also there is one more layer of infor-mation of a less specific kind: the supply-chain for thematerial, the codes, regulations and standards that deter-mine its acceptability in a given application, the avail-ability of specialized stress-analysis or optimization toolsto enable its use in design—what might be called theinfrastructure needed to support its use.

Design is by its nature an iterative process. The initialrequirements lead to a first selection that suggestsmodifications to the requirements. If they are insuffi-ciently restrictive too many solutions are obtained; ifover-restrictive, none may remain, requiring a relaxationof constraints or the need to develop a completely newmaterial or hybrid of two or more materials(see below).As a result, the designer must correct, modify and re-specify his requirements and repeat the selection proce-dure. This puts strict requirements on selection tools:they must allow this iterative procedure and accommo-date a range of databases appropriate to the variousstages of the design.

A simple architecture that meets these requirementshas two main components. The first is a set of databasesstructured hierarchically(see Fig. 3), with data attachedto records at each level of the hierarchy so that they canbe used at the required level of precision and selectionalgorithms that allow ease of iteration. A crucial pointis that these databases need to be comprehensive andcomplete, with data for every property of every material,requiring innovative methods for estimating propertieswhere data are missing. The second component is aninformation system that can manage large amounts ofsupporting information in a range of data formats, whichcan be searched for background on a particular materialby keywords or full-text searching. These are the basic

principle underlying the development of the software ofthe ‘CES family’ (CES 4, 2002).

3. Selection strategies

That, then, is the nature of the information. What arewe going to do with it? Action is guided by strategy;strategy is shaped by the outcome that is sought and themeans available to achieve it. A selection strategy hasthree components:

a The formulation of constraints that must be satisfiedif the material is to fill the desired function;

b The formulation of a performance metric or valuefunction to measure how well a material matches aset of requirements; and

c. A search procedure for exploring solution-space, iden-tifying materials that meet the constraints and rankingthem by their ability to meet the requirements.

Developments of selection strategies can be found inthe texts by Dieterw1x, Charles et al.w2x, Farag w3x,Lewis w4x, Ashby w5,6x, Brechet et alw7x and ASM w8x.´All, ultimately, seek ways to implement the processsuggested by Fig. 4: it is to convert a set of inputs—therequirements of the design—into a set of outputs—aselection of material and process. The role of the strategyis that of a transfer function. The second and third rowssuggest two broad classes of transfer function: that offree searching using quantitative analysis, that of exploit-ing expertise capture in a questionnaire, and that ofseeking previously-solved problems with features likethat of the current problem(analogy).

3.1. Free searching, based on quantitative analysis

Quantitative analysis, when it works, it is fast andefficient; it offers great freedom of application; and ithas the ability to reveal solutions that are new andinnovative. However—and here is the but—it needsprecisely detailed inputs in a form that can be analyzedby standard engineering methods. ‘Find me a materialfor a beam of lengthL to support a forceF attemperatureT, and make it as light as possible’ can beanalyzed by standard engineering methods. ‘Find me amaterial to insulate my house’ cannot be approachedwithout prompting about the type of house, the locationin which it will be built and the building codes for thatlocation.

The steps in quantitative analysis are listed in Fig. 5.First establish the function of the component, the con-straints it must meet and the objectives that are thetargets for the design, and the variables that the designeris free to choose to meet the objectives—among them,the choice of material. The function of the beamdescribed above is to carry bending moments—that is

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Fig. 3. The organization of structured data of the sort generally found in handbooks and data sheets. The material or process is indexed by name;the information listed under the name gives numeric values for material properties, a ranking of performance under standard conditions and yesynocategorization of material–process compatibility.

the definition of a beam. It must support the designloads without deflecting too much or failing and it mustsurvive at temperatureT (constraints). Further, it mustbe as light as possible(the objective—that of minimiz-ing weight). Selection proceeds in three steps: the stepsof screening, ranking and supporting information illus-trated in Fig. 6. The screening step has the function ofeliminating materials and processes cannot meet theconstraints. The ranking step orders the admissiblesolutions using performance metrics base on the objec-tive. In the supporting information step, specific infor-mation is gathered about top-ranked candidates, someof it dependent on the local situation of the company.

Returning to the example of the beam, the constrainton the use-temperatureT screens out potential candidatesthat cannot carry load atT, drawing on structured datafor the maximum use temperature of materials to do so.The objective of minimizing weight whilst supportingthe design load allows the survivors to be ranked—again drawing on structured data. The uppermost of theranked list become the prime candidates. Unstructureddata for these is explored, seeking insight into theirdeeper personalities, enabling a final choice.

Software to support decision-making of this kind takesthe form illustrated by Fig. 7. Structured data formaterials and processes are stored in a relational data-

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Fig. 4. Selection strategies act as transfer functions, converting design requirements into a listing of selected material and processes, withbackground information about each. Here we consider strategies, listed in the lower part of the figure.

Fig. 5. The starting point for rational analysis is the identification offunction, objectives and constraints. The method is illustrated in Sec-tion 3.1.

base structure: materials information in one suite ofrecords, process information in a second such suite.Records in the materials suite are linked to thoseprocesses that can be applied to them; consequentlyrecords in the process suite are linked to materials towhich they can be applied. Records in both data tablesare linked to text files and to web sites that containunstructured supporting information.

The method is most easily understood through anexample. Fig. 8 shows the beam mentioned before. Forsimplicity, it has a square cross-section with an area1

Asb . It is required that the beam should be as light as2

possible while having a specified stiffnessS and that itshould operate without failure at 5008C. The function,constraints, objective and free variables are listed in Fig.9. We choose the massm as the objective function; itis to be minimized. We write

msALr (1)

whereA is the cross sectional area,L is the length andr is the density of the material of the beam. Thestiffness constraint sets a lower limit forA. The bendingstiffness of a beam,S, is

2C EI C EA1 1Ss s (2)3 3L 12L

whereE is the modulus of the beam material,I is thesecond moment of area of its cross-section, andC is a1

A similar treatment for a beam of arbitrary cross-section shape1

leads to a similar material index as that derived here containing a‘shape factor’ that accounts for the shape of the cross-section—seeAshby w5x for details.

constant that depends only on boundary conditions andthe way in which the loadF is distributed. From Eq.(2)

1B E 312S 2C FAs L (3)2D GC E1

which, when substituted into Eq.(1) gives an expressionfor the massm (the objective function):

1B E B E512S r2C F C Fms L (4)2 1y2D G D GC E1

The quantitiesS, L andC are specified by the design.1

The lightest beam is that made of the material with thegreatest value ofE yr, which is called a material1y2

index. There are many such indices, each describing acombination of objective and constraint—a few areindicated in Fig. 10.

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Fig. 6. The different stages for materials and process selection in design and the conceptual tools to carry them out.

Fig. 7. Typical contents and linkages in a relational database for selecting material and processes. The individual suites of records(circles) containrecords for individual material and processes and their attributes, as suggested by Fig. 3.

Fig. 8. A beam loaded in bending. In this configuration,C s48.1

We now have the information we need for screeningand ranking. Initially, all materials are candidates. Usinga database of material properties, software such as the

CES4 w24x materials selection system allows the con-straints to be applied(Fig. 11). Applying the constraintthat the maximum working temperature of the materialmust exceed 5008C (T )773 K) eliminates all thosemax

in the gray box in the lower part of the figure. Thesame software can be used to generate the plot ofEagainstr shown in Fig. 12. The scales are logarithmic,with the consequence that the criterionE yrsC plots1y2

as a straight line with a slope of 2. The CES selectorallows this line to be moved until only a few materialsare left above it; these are the ones with the largestvalues ofE yr. The system combines the two stages1y2

(or more, if wished), listing their intersection—that is,

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Fig. 9. The design requirements, expressed as in Fig. 5. The analysisgiven here is for the square section beam; a similar analysis for themore realistic shapes(I-beams, tubes, etc.) gives the same materialindex.

Fig. 10. Material indices are the property groups that emerge from rational analysis of the performance of a component. This diagram showsthose relevant to the design of a beam of minimum mass(the objective).

the materials that haveT )500 8C and exceptionallymax

large values ofE yr. Supporting information can then1y2

be sought for these using handbooks, journals and theauthoritative web-sources such as the ASM Handbooks(ASM 2002), retrieving past experience, design guide-lines, commentaries on behavior in known environments,eco-hazards, toxicity and so forth. The user now hassufficient information to make an informed choice.

The method is fast, systematic and has the potentialfor innovation: if the material database contains materi-als that have never before been used for light, stiffbeams or if completely new materials are added to it,they will appear in the selection if they properly fill thedesign specification. The method is now well developedand has been applied successfully to material selectionproblems in mechanical and electro-mechanical designas well as process selection. The method is now welldeveloped, techniques for meeting many constraints andmore than one objective exist(Ashby w6x; Brechet et al.´

w8x), and software and data to support the activity arecommercially available—CES4w24x is an example.

Its use does, however, place certain demands on theuser, who must be able to formulate the design require-ments precisely, to develop one or more performancemetrics and express them in the form of indices(orknow where to find them) and have the experienceneeded to find unstructured data and draw balancedconclusions from them.

3.2. The questionnaire strategy, based on expertise-capture

Questionnaires guide the uninformed user through amore or less structured set of decisions, using built-inexpertise to compensate for the lack of it in the user.Questionnaires to guide selection of materials and pro-cesses are constructed by documenting the ways inwhich experts do it. However—here, too, there is abut—eliciting this information is not easy(‘first captureyour expert’). The method, in principle, is to presentthe expert with a comprehensive set of specific questionsand solicit from him or her the answers and the furtherquestions that follow from those answers and the spec-trum of answers to these, and the further questions untilan unqualified answer is reached. Also therein lies thedifficulty: there has to be a definitive answer to everyquestion, and further questions following from everyanswer until the end is reached; nothing can be lefthanging. The construction carries an enormous overheadin time and a still greater one in patience—above all,that of the central character, the expert. Also—a smalladditional consideration—not all experts agree.

Therefore, it can be done, and when done well itachieves exactly what is wantedw10,11x. However, ithas to be accurately targeted. Thus, a questionnaire toguide selection of aluminium alloys for die-castings is

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Fig. 11. Applying simple constraints: here, that on modulus. Young’s modulus for a range of materials is plotted here. Those in the grey zone(E-10 GPa) are screened out. All polymers are eliminated.(Figure generated using the CES4, 2002 selection software.)

possible(and exists) w12x, as are others that map tightlylimited domains. Such methods have their own specialstrengths. First, the novice can be trusted(or can trusthimself) with its use—there is the pedigree of the expertbehind it. Also by limiting its domain, a questionnairecan offer greater resolution. However, it does not inno-vate—the selections that emerge are those alreadyknown to the expert. Also a new material or process islost here—the questionnaire will not recover it becauseit did not exist when the expert was consulted.

A survey of current questionnaire-based selectors formaterials and processes reveals a diversity of interfaces.We take the selection of processes for joiningw13,14xas an example. At the diffuse end of the range is the‘locate yourself in these lists’ interface. The user ispresented with columns of options like those in Fig. 13,each addressing a single question. What materials doyou want to join? What is the geometry of the joint?How is it loaded? What additional features do you want?The questions can be answered in any order and anycombination—the boxes suggest one combination.

Thus, far all we have is a constraint-based selector,something that can be done efficiently using the method

of Section 3.1. The expertise appears in the weightings—the odds, to take a racing analogy—the expert hasattached to each of the choices. MIG-welding of lowcarbon steels? 5y4 on. Soldering of aluminum? 30y1against. By weighting each choice and combining theweightings, a useful ranking is possible; material-processcombinations with favorable odds win, those with unfa-vorable are dropped. This ability to rank material-process combinations is an example of the addedresolution that the questionnaire approach allows: some-thing that is harder to build into analysis-based methods.

At the other end of the range is what might be calledthe narrow band-pass filter. Here all freedom of choiceis gone; the user is led through a sequence of yesynoquestions, each answer triggering a further compulsorychoice, as illustrated by Fig. 14. No chance of a mistakehere—the expert takes all responsibility; the user isforced to follow a pre-determined path, with no answeruntil the end of the chain of questions is reached. Thesimplicity and ease of use are obvious; the obviousdifficulties lie in its creation and maintenance. However,the method does have one attraction: it offers still higherresolution. Many processes can butt-weld metal plates,

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Fig. 12. Ranking, using the material indexE yr. The best choices are those that meet the constraint of Fig. 11 and have high values of the1y2

index. (Figure generated using the CES4, 2002 selection software.)

but the best choice—meaning the most economical—depends both on the material and on the thickness ofthe plate. Experts know this from experience and if theycan be induced to share the experience it can beincorporated into a domain-specific questionnaire.

3.3. Inductive reasoning and analogy

Inductive reasoning has its foundations in previousexperience. Here the inputs are design requirementsexpressed as a set of problem features; the transfer-function exploits knowledge of other solved problemsthat have one or more features in common with the newproblem, allowing new, potential solutions(‘hypothe-ses’) to be synthesized and tested for their ability tomeet the design requirements.

A central feature here is the library of previously-solved problems or ‘cases’w15x—a ‘case’ is a problem,an analysis of its features, a solution and an assessmentof the degree of success of this solutionw16x. Thechallenge in assembling the library is that of appropriateindexing—attaching to each case a set of index-wordsthat capture its features. If the index-words are toospecific the case is only retrieved if an exact match isfound; if too abstract, they become meaningless to

anyone but the person who did the indexing. Consider,as an examplew17x, the ‘case’ of the redesign of anelectrical plug to make it easy to grip, insert and pull-out by an elderly person with weak hands. Indexing by‘electrical plug’ is specific; the case will be retrievedonly if the ‘plug’ is specified. Indexing under ‘designfor the elderly’ is more abstract and much more useful.Plugs are not the only thing elderly people find hard touse. Cutlery, taps, walking sticks and many other prod-ucts are adapted for elderly people. Examining theirshapes and materials and processes used to make themmay suggest new solutions for the plug.

The use of analogy-based selection is made clear byFig. 15. The new problem is analyzed and its featuresidentified. The library is searched for ‘cases’ withfeatures in common with those of the new problem. Theretrieved ‘cases’ document materials and processes usedto create these features. The new problem is tackled and(with luck) solved by adapting and combining elementsof the selected ‘cases’ to meet the new need.

3.4. Which method, when?

Each strategy has its strengths and weaknesses. Thereis room for all three, used singly or together. Here is anexample.

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Fig. 13. The simplest sort of questionnaire asks the user to select from categories, as illustrated by the boxed selection show here.

Fig. 14. The structured questionnaire. The user is led through an ordered series of questions. The system must have responses for every possiblecombination of choices if the user is not to be left hanging part way through, creating a heavy overhead in construction and maintenance.

The challenge: to choose materials for thermal insu-lation in two very different applications. The first: anX-ray telescope to be launched as part of the EuropeanSpace Agency Program, and temperature-sensitivebecause it relies on diffraction from a single crystalsilicon lattice. The second: insulation for the walls of ahouse in San Diego, California, where, at the time ofwriting, there is an embarrassing energy shortfall.

The first—the telescope—is a new problem seekinga novel solution. It must be light in weight, it will seeextremes of temperature, and it is exposed to UV andother radiation. Here strategy 1—quantitative analysis—

works well. The requirement of maximum insulation atminimum weight can be expressed as an index(‘seekmaterials with high values of 1yrl where r is thedensity andl is the thermal conductivity’). We applythe constraints that the insulating material must tolerateUV radiation and the extremes of temperature and rankthose that do by the index 1yrl. We then seek support-ing information about availability, shaping and joiningof the top-ranked candidates, past experience of use inspace and so on. However, analysis is not the onlyroute. Strategy 3—seeking ‘cases’ with features in com-mon with those of the X-ray telescope—might give

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Fig. 15. Selection by inductive reasoning. A new ‘solution’ is synthesized by combining elements of other solutions to problems with featuresthat resemble that of the current problem.

useful results. A search on Design of Precision Instru-ments, which frequently have stringent requirements forthermal control and on other space applications in whichthermal insulation is critical(liquid hydrogen lines, forinstance) might provide helpful information.

Now the second—the house in San Diego. Here thedomain is narrow—that of thermal insulation for privatebuildings. All countries have their building codes, andthey differ; the codes dictate requirements that dependon the use of the building and the place in it for theinsulation. The first question, then, is: which country?OK, what sort of building? And what part of thebuilding? and more. Analysis is not of much help here;instead, strategy 2—a structured questionnaire dedicatedto the narrow domain of insulation for buildings—is asurer approachw18x.

4. Refinements: identifying value functions andexploring solution space

4.1. Identification of the value function

The index method, illustrated in Section 3.1, measuresthe efficiency of a given material for a given elementaryfunction (a beam, for instance) with a prescribed con-straint and a single objective. Real problems are seldomthat simple. Most involve many constraints and multiple,often conflicting, objectives(minimizing both mass andcost, for example). The selection must take account ofall of these.

When dealing with multiple constraints, it is necessaryto identify the limiting constraint, requiring that each bemodeled in the manner of Section 3.1. When there aremultiple objectives, it is necessary to combine them intoa single ‘value function’. This requires an ‘exchangerate’, a, between two objectives, measuring the valueof one in units of the other: for instance when bothmassm and costC are to be minimized, it is necessaryto estimate the acceptable cost to save 1 kg. The two

can then be combined into a single locally linear valuefunction V:

VsamqC (5)

for which a minimum is sought. The exchange constantcan be obtained from a functional analysis of the objector from the analysis of existing solutions(Ashby w9x).Software tools to assist with these tasks have beendeveloped—the CAMD and VCE packagesw19–21x.

These two techniques—the coupled equation methodand value analysis—allow the derivation of a valuefunction that describes as objectively as possible theability of a given material to meet a complex set ofrequirements. However, the extra information requiredto operate these methods are not always available. Analternative is to identify the relative importance of thevarious criteria. One way is to assign ‘weighting factors’to each objective, but they are heavily dependent on thejudgement of the person assigning the weights. This canbe partly overcome by the used of ‘Fuzzy logic’. Therequirements in terms of properties or performanceindices are expressed with some flexibility: for instancea minimum Young modulus is required, with a toleranceof 5% and a given strength with a tolerance of 20%.This allows for the possibility to trade off one propertyfor another in a complex design. Another approach isto propose to the designer solutions that are at themargin of his requirements and to ask for a qualitativereaction to these solutions. The computer will then buildan ‘aggregation function’(i.e. it will select a weightedaverage of the various criteria, arithmetic mean, geo-metric mean or more complex functions) which wouldgive a reaction similar to that of the designer. Thismethod, which has been implemented in the software‘Fuzzymat’ w22,23x, bypasses the difficulty of identify-ing a priori the weighting coefficient. However, itintroduces in the procedure some subjectivity, while the‘coupled equationsyvalue analysis’ approach is totallythe objective.

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4.2. Strategies to explore the solution space

When a ‘evaluation function’ has been defined froma quantitative analysis of the set of requirements, it isnecessary to explore the solution space. Several tech-niques have been used; the most efficient one dependson the nature of the solution space.

When the solution space is a finite list of discretematerials and processes(such as the data bases of CES),the most efficient way to explore this solution space isa simple ‘screening algorithms’: the evaluation functionis calculated for each admissible solution of the database(i.e. all those that have passed the first screening stage)and the solutions are ranked in order of decreasingefficiency (CES 4, 2002) w24x.

When the solution space is in principle infinite, butcontinuous(such as for instance in the case of optimiz-ing the composition of a glass for a given set ofrequirements on its properties) standard optimizationtechniques such as the Simplex algorithm or steepestgradient methods perform efficiently the job of findingthe optimum of the evaluation functionw22x.

When the solution space is infinite but non-continuous(such is the case for composite materials optimizationor sandwich structure optimization, where the materialschoice is discrete and the geometrical variables iscontinuous) genetic algorithms have proved to be effec-tive w23–25x.

5. Developing specialized software tools

All the methodologies described in this paper havebeen applied at one time or another to the selection ofmaterials and processes. Table 1 lists existing selectiontools built with the different strategies. Free-search toolsusing strategy 1 are well suited for the early stages ofdesign and have the ability to suggest innovative solu-tions. As the design becomes more specific, morefocused tools are needed. Some couple search methodswith micro-mechanical models to generate the space ofpossible solutions(for instance for optimizing compositematerials or sandwich structures). Others use the ‘ques-tionnaire approach’. Recently, the ‘analogy’ strategy hasbeen illustrated on a selector for joining processesw17x.The key issue is here to develop a tool to measure theproximity between cases. Cluster analysis and Multi-dimensional scaling(MDS) are possible strategies.Another possibility is to take advantage of existingknowledge to structure the cases by their positions on‘trees’ corresponding to a predefined questionnaire(likethe material and process trees of Fig. 3) and to measurethe distance between cases by the distance between theirpositions in the treew26x.

From these examples, a number of simple rules fordeveloping specialized software have been identified.The databases for a ‘free search strategy’ are best

constructed with a hierarchical structure like that of Fig.3. The databases should not have any missing informa-tion, all the families of the database should be repre-sented and the attributes should be identical for all theelements of the database, allowing comparability. The‘questionnaire approach’ requires that all the questionsshould be meaningful and referenced for all the elementsof the database. A structure like that of Fig. 13 allowsthat all the questions are asked, but all are not necessarilyanswered; the discrimination increases with the numberof answers. A structure like that of Fig. 14, by contrast,requires that all questions are answered, and in aprescribed order, giving high discrimination—providedall the answers area known.

The variety of applications is representative both ofthe versatility of the procedures and of the variety ofrequirements encountered at various stages in the designprocedurew36x. If the general methods can be appliedin the first stages of the design procedure, the questionof expertise is central to the further stages. Two ques-tions have to be answered. What is the most efficientway of storing this expertise: a well-structured question-naire or a rich database of cases with an appropriateexploring algorithm to guide the analogies? How canthe stored expertise be a source of information forinnovative design without being an encouragement toconservatism? Both these questions are still to beanswered.

6. The future

There are many unsolved problems in the field ofoptimal selection and many challenges in adapting themethods to meet specific requirements. Most requirethat a greater degree of modeling be incorporated intothe selection procedures. The purpose of this last sectionis to outline pending questions requiring more researchand to stimulate readers to contribute to this field.

6.1. Finite time design and expertise retrieval

The selection methods described thus far do notinclude a consideration of component life. As a generalrule, life is limited by creep, by fatigue, by corrosion orby wear. The first two depend only on the properties ofthe material and the way it is loaded; data for creep andfatigue behavior can be stored in data-structures likethose already described. Corrosion and wear are moredifficult because they depend not only on the propertiesof the component-material but also on those of theenvironment in which it is used(the corrosive medium)or the counter-face on which it rubs and the lubricant(if there is one) in between.

Methods exist for safe design with a given materialunder conditions of creep, fatigue, corrosion or wearw37x. However, selection is more difficult—it requires

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Table 1Existing software for selection relying on the methodology of this paper

Name of the software Objective of the software Type of strategy Evaluation method

CES Materials and process selection, Free searching Screening of the(Granta Design, 2002) databases for materials and databasew24x processes. A number f specialized

database on materials andprocesses and possibility to createnew ones

Fuzzymat Materials and process selection, Free searching Screening on the(Bassetti, 1997) same databases as CES, multi- databasew22,23x criteria selection using fuzzy logic

algorithm

CAMD Materials and process selection, Free searching Screening of theLandru, same databases as CES, expert and database by aw19,27x guide for developing the set of questionnaire recursive algorithm

requirements implementation ofcoupled equation and value analysis

Fuzzy composite Optimization of composite Free searching Genetic algorithmPechambert Duratti materials , matrix, fiber, and and screeningw23,28x architecture. Expert compatibility Micro-mechanics

database for process. models to createpossible solutions

Sandwich selector Optimization of materials selection Free searching Genetic algorithmLemoine and dimensioning for structural and screeningw28,29x Sandwiches Mechanics modes

to create possiblesolutions

Creep selector Selection of polymer in creep Free searching PhenomenologicalLemoinew46x design modelling and

screening method.

Fuzzyglass Optimization of glass composition Free searching Simplex algorithmBassetti,w22x for properties and processability. coupled with fuzzy

Database of correlation logiccoefficients.

Fuzzy extrude Optimization of aluminum extruded Questionnaire ScreeningHeiberg,w30x alloys selection, including

extrudability and shape via expertrules

Fuzzycast Optimization of aluminum cast Questionnaire ScreeningBassetti, alloys selection, including hotw22,12x tearing and mould filling via expert

rules

STS Selection of surface treatments Questionnaire ScreeningLandru, according to the compatibility with Proximity on a treew27,31x the base material and the required

function

VCE Identification of value coefficients in AnalogyLandru a design procedure from existingw27,32x solutions

MAPS Identification of possible Free searching ScreeningLandru applications for a material from aw27,33x propertiesy performance profile

Astek Selection of optimal joining Questionnaire ScreeningLeBacqw13,14x methods

Astek expert Selection of optimal joining Analogy, case Proximity on aLae w17x methods from existing solutions based reasoning tree

CES aesthetics Suggestion for industrial design Analogy, caseJohnsonw34x from a database of objects based reasoning

Failure expert Guide to failure analysis and Analogy, caseBougetw35x possible solutions from a database based reasoning

of cases

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that the response of all candidate materials are simulta-neously evaluated and compared. The only practical wayforward is to develop semi-empirical models(‘consti-tutive laws’) for each mode of failure, fitting them tothe incomplete data available in compilations such asthose of the ASM(2002) Handbooksw38x or the NIMS(2002) data sheetsw39x and others like them. Selectionfollows procedures that are parallel to those alreadydescribed, with provision for the operator to enter thedesign conditions—the temperature and design life inthe case of creep or theR-value and fatigue life in thecase of fatigue, allowing allowable stresses to be cal-culated. These ‘allowables’ then replace the elastic limitin the indices for minimum mass and minimum costdesign outlined in Section 3.1. Examples of the imple-mentation of this method for creep and fatigue can befound in CES4w24x.

Corrosion and wear present greater challenges, as yetincompletely resolved except in narrow applicationdomains such as the design of aircraft structures forwhich comprehensive data-sets can be assembled. Muchinformation about both is available only as ‘expertise’,recorded as design guidelines about material compati-bility and preferred geometry(‘avoid metal coupleswhen aqueous corrosion is a possibility’, ‘sliding alu-minum on steel can lead to scuffing’, ‘Titanium alloy Xis susceptible to stress-corrosion cracking in salt water’,‘liquid at rest in a container can lead to water-linecorrosion’). Such information is readily stored as text;the challenge is to capture, index and retrieve it accu-rately and at the relevant point in the design.

6.2. Process selection and modeling

The process selection strategies described thus farrely on pre-stored attributes of the process, whichinclude the materials it can treat, the shapes it can createand the characteristics of the finished product—its sur-face finish, tolerance, etc. This enables screening toisolate those processes that that can meet the require-ments, using the wide-ranging free-search method ofSection 3.1 or higher resolution in a narrower domainusing the questionnaire-based method of Section 3.2.However, neither of these captures the full complexityof the material–process interaction. Here, too, modelingis the way forward—the modeling of interest here isthat which is instrumental in selecting the process. Thus,modeling of the heat transfer from object to mould, ininjection molding, allows prediction of the time beforethe mould can be opened and the part ejected. This time(and thus the rate of production) can be decisive indeciding whether injection molding is economicallyviable.

Intelligent process selection could go further, predict-ing the process settings required for a given application.As an example: laser welding can be used to butt-weld

many metals, but the optimal laser power and trackingvelocity depend on the metal and on the plate thicknessand the condition of its surface. Modeling can capturethis information and—if based on physical understand-ing—can allow extrapolation to suggest optimal condi-tions even when no real data are availablew40x. Thus,the need here is for coupling between process selectionand process modeling. It is unlikely that a generalformat can be found in this direction, but weldingtechnologies and surface treatments are promising fieldsof application for this approach.

6.3. Interfacing of the materials and process selectiontools with geometric modeling and dimensioning tools

Material and process selection are one part of a largerprocess—that of choosing the shape and dimensions ofa component. Ideally, these activities should be coupledso that the geometric modeling correctly scales dimen-sions and creates features that make the best use of thematerial and the process and avoids those that do not.Many materials databases have the ability to export datain a format accepted by finite element packages. How-ever, this ‘passive’ coupling is only a first step; it doesnot deal efficiently with the coupling between the shape,material and process. Car wheels, for instance, can bemade in cast aluminum and in stamped and weldedsteel, but they are so made in totally different shapes tomatch the capabilities of very different processes. Thereis a need for an ‘active coupling’ between FE calcula-tions, CAD tools and the materials and process selector.A requirement here is the ability of the CAD tool torecognize features, and an ability to index processes andmaterials with the features that they can create. In thisway the designer can be warned of the restrictive natureof features that might then be changed and the materialsselector can be warned of the restrictions that a com-mitment to a given process places on the design.

6.4. Multi-materials selection

When no single material can meet all the designconstraints or offer sufficiently high performance, asolution may be found by combining two or morematerials to form a hybrid ‘multi-material’. Materialsselection is incomplete if it fails to deal with thepossibility of selecting woven composites, sandwichesor multi-layers. This requires the ability to optimizesimultaneously materials choice and multi-materialsgeometry. Three questions need to be addressed: whenis a multi-material approach necessary because of theincompatibilities of the set of requirements? What arethe relevant ‘type geometries’ worth investigating(cables, sandwiches and stiffened plates, filled tubes,etc.) and their advantges? What are the optimal choicesfor the materials and the dimensions of the chosen

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geometry? Partial solutions to these problems exist, butan overall strategy is still to be developed and imple-mented into software. The whole question is of criticalimportance when redesign is considered: it is importantto start from the functional analysis and to evaluate thepossible multi-materials solutions at the same level ofgenerality with which the ‘free search methods’ currentlydeal with a single material selection(see for instanceKromm et al.w41x).

6.5. Green design

Design to minimize adverse impact of engineeringproducts on the environment(‘green’ design) is assum-ing an increased importance in all branches of engineer-ing. Eco-impact thus becomes an additional metric tobe optimized, along with performance and cost. Theproblem is a complex one: eco-impact can be associatedwith the extraction and refinement of the material, withthe manufacture of a product from it, with the use ofthat product and with its disposal(Wegst et al.w42x).Well-intentioned design to reduce the impact of onephase of life may have the effect of increasing it inanother; it is a systems problem, not one of isolatedevents. Limited progress has been made, from whichthe obstacles can now be recognized. Briefly, they arethese: what material attributes, not already available, arerelevant for green design? How are these attributes tobe measured and stored? And how should they bemanipulated to minimize whole-life impact rather thanjust that of one part of the life cycle?

6.6. Aesthetics and industrial design

The consumer in any developed country has, today, awealth of choice. Any given product is available inmany models, all with almost the same functionalityand price. The choice the consumer makes is thendetermined by his or her perception of the product: itsaesthetics, its associations, what it means to them. Muchof this perceived value is created by the materials ofwhich the product in made and the processes used tomake and, particularly, finish it. Industrial designersexpress the wish for materials selection systems to assistin this choice. The challenge here is to identify therelevant attributes and devise ways of search solution-space that can meet their need. Some progress has beenmade but the methods are in their infancy(see, forexample, Ref.w34x).

6.7. Selecting functional materials

The phrase ‘functional materials’ needs clarification.Much of the time it is used to distinguish materials thatare primarily used for non-structural purposes—it istheir thermal, electrical, magnetic and optical properties

that are exploited. We shall refer to these as ‘passive’functional materials to distinguish them from ‘active’materials that respond to a signal rather than simplytransmitting one: materials with piezo-electic, magneto-strictive and other such behavior.

Data for passive functional materials can be found inhandbooks and databases; the methods for selectingthem and the processes required to shape them aresimple extensions of those described above. There isscope for exploring in greater depth the use of indiceslike those of Fig. 10 for the optimal selection ofelectrical conductors and insulators(which must oftenperform secondary mechanical or thermal functions atthe same time), for hard and soft magnetic applicationsand for the optical properties of glasses.

Active functional materials pose greater challenges.First, the coupling they provide between two differentkinds of signal(mechanical and electoral, thermal andmechanical, thermal and electrical, magnetic andmechanical) is always associated with directionality,requiring a tensorial description or at least the determi-nation of more than one interaction coefficient. Second,the response frequently depends on shape or can bemagnified by shaping, so that selection cannot be decou-pled from shape. Also third, the ‘functionality’ theyoffer includes their ability to act as sensors and actuators,giving them a much richer set of attributes that includethe frequency at which they can be driven, the powerthey can convert and the efficiency with which they doso w43x.

6.8. The challenge of miniaturization

In the design of consumer electronics(mobile phones,laptop computers, PDAs, etc.) two of the dominantobjectives, today, are the minimization of size andweight. Similar objectives influence the design of mili-tary equipment, driven by the need to pack ever morefunctionality and ever less space. Miniaturization impos-es greater demands on materials: the intensity ofmechanical, thermal and electrical loads all tend toincrease as the size diminishes. This creates new oppor-tunities for innovative selection, evident in the increaseduse of specialized materials for heat transfer(aluminumnitride heat sinks with metal foam heat exchangers) andof high strength, low density materials for chassis andcasings(titanium and magnesium cases for lap tops andmobile phones, for instance).

In these examples the scale, though reduced, remainsin the range in which materials exhibit normal ‘bulk’properties. However, there is a growing interest inmanufacture at an even smaller scale. Micro electro-mechanical systems(MEMS) aim at creating electro-mechanical(as opposed to opto-electronic) functionalityat the millimeter-to-micron scale. As with more conven-tional miniaturization, this imposes greater loads on

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materials, but their properties, too, change with scale.The mechanical properties of thin films can differ greatlyform those of the bulk material, and the ability, at themicron level, to create multi-layers and to utilize theprocessing methods of the micro-electronic industry toform complex multi-material combines, poses new chal-lenges for the capture and storage of information aboutmaterials and processes and to model the process–material–shape interactionw44x.

6.9. Identifying possible applications for new materials

The inverse problem is an interesting one. Rather thanseeking materials to fill a new application, how can weidentify applications for a new material? Developing anew material is a slow, expensive process; to do sowithout knowing how it will be used(historically acommon enough occurrence) may be a poor investmentof time and money. Can a procedure be developed totake out some of the risk? Attempts have been made(w45x), largely based on a detailed comparison of thevalues of the properties and the indices(see Fig. 10)with those of existing materials, but there remains scopefor the development of more refined methods andsoftware to support them.

7. Conclusions

In this paper we have given an overview of systematicmethods to guide the selection of materials and pro-cesses. The emphasis has been on the strategies thathave some demonstrated degree of success and gener-ality. A number of teams are working on specific areas(corrosion, surface treatments, DFX methodologies, etc.)and further progress in the field requires closer collab-oration between various sorts of engineering expertise.

Dealing with the full complexity of problems ofmaterials and process selection leads to challengingoptimization issues because of the complex topology ofthe solution space. The work performed by two of theauthors on the application of genetic algorithm stemmedfrom discussions with a specialist of spin glasses!w28x.It seems clear to us that some well-known techniquesin statistical physics(neural networks, simulated anneal-ing) are worth exploring as possible solutions to thesedifficult problems.

The increasing need to store and retrieve expertise inengineering field will also require input from artificialintelligence experts, specialists of automatic translation,of sentence recognition, intelligent indexing and devel-opers of web exploration engines.

The expanding field of micro electro-mechanical sys-tems (MEMS), and more generally the domain offunctional materials is also a source of interestingproblems for these methods, requiring a merging of the

skills of the physicist, the materials scientist and theengineer.

The present paper is to be read as an incentive forthe various fields of expertise to contribute to this newemerging field of ‘selection for design’.

Acknowledgments

Many people have contributed to the ideas outlined,only too briefly, here. We particularly wish to thank DrH. Shercliff, Dr K. Johnson of the Engineering Depart-ment, Cambridge University, Dr D. Imbault from INPG,Dr V. Mandrillon from CEA Grenoble, for many helpfuldiscussions, and to acknowledge the support of theKorber Foundation and of the U.K Engineering and¨Physical Science Research Council through a grant tothe Engineering Design Centre at Cambridge.

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