Material screening and choosing methods – A review

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Material screening and choosing methods – A review

A. Jahan a,*, M.Y. Ismail b, S.M. Sapuan b, F. Mustapha c

a Department of Industrial Engineering, Islamic Azad University, Semnan branch, P.O. Box 35136-93688, Semnan, Iranb Department of Mechanical and Manufacturing Engineering, Engineering Faculty, University Putra Malaysia, Malaysiac Department of Aerospace Engineering, Engineering Faculty, University Putra Malaysia, Malaysia

a r t i c l e i n f o

Article history:Received 23 June 2009Accepted 9 August 2009Available online 12 August 2009

Keywords:Material selectionMulti-criteria decision makingSelection for material properties (H)

a b s t r a c t

The selection of a material for a specific engineering purpose is a lengthy and expensive process. Approx-imately always more than one material is suitable for an engineering application, and the final selection isa compromise that brings some advantages as well as disadvantages. One of the issues that emerges fromthis review is that regardless of the relation of design stages and process selection with material selection,screening and ranking are two vital steps in the material selection. A variety of quantitative selection pro-cedures have been developed to solve this issue, so that a systematic evaluation can be made. This paperseeks to address the following questions: (1) what is the contribution of the literature in the field ofscreening and choosing the materials? (2) What are the methodologies/systems/tools for material selec-tion of engineering components? (3) Which approaches were prevalently applied? (4) Is there any inad-equacy of the approaches? This research not only provides evidence that the multi-criteria decisionmaking approaches has the potential to greatly improve the material selection methodology, but also aidsthe researchers and decision makers in applying the approaches effectively.

� 2009 Elsevier Ltd. All rights reserved.

1. Introduction

Manufacturers and engineers are always on the lookout for newmaterials and improved processes to use in manufacturing betterproducts, and thus maintain their competitive edge and increasetheir profit margin [1]. In recent years, many traditional materialswhich have served in engineering applications for a long time arebeing replaced by the so called ‘new materials’, in order to meetthe demand of weight reduction and performance enhancement[2]. The available set of materials is rapidly growing both in typeand number [3]. It is estimated that there are more than 80,000materials in the world. It includes metallic alloys and nonmetallicengineering materials such as plastics, ceramics and glasses, com-posite materials, and semiconductors. This large number of materi-als, coupled with the complex relationships between the differentselection parameters, often make the selection of a materials for agiven component a difficult task [4]. In selecting materials, design-ers and engineers have to take into account a large number of fac-tors. These factors for material include mechanical properties(Young’s Modulus, strength, yield stress, elasticity, fatigue, creepresistance, ductility, hardness and toughness), physical properties(crystal structure, density, melting point, vapor pressure, viscosity,porosity, permeability, reflectivity, transparency, optical properties,dimensional stability), magnetic properties, electrical (resistivity,permittivity, dielectric strength), thermal and radiation (specific

heat, conductivity, expansively, diffusivity, transmissivity, reflec-tivity, emissivity), surface (texture, corrosivity, wear resist), manu-facturing properties (machinability, formability, weld ability, castability, heat treatability, etc.), material cost, reliability, durability,recycle ability, material impact on environment, performance char-acteristics, availability, fashion, market trends, cultural aspects,aesthetics, etc. Beside these aspects, recently user-interaction as-pects such as appearance, perceptions and emotions are consideredin material selection [5–8]. The user-interaction aspects are thosethat effect on the usability and personality of a product and formu-lated as sensorial material properties. Materials get different mean-ings in various products. Recent research by Karana et al. [9]showed association of certain materials with certain meanings suchas aggressive, nostalgic, professional, sexy and toy-like.

Materials are sometimes chosen by trial and error or simply onthe basis of what has been used before, while this approach fre-quently works. Not long ago, materials selection was considereda small part of the design process [10]. The selection of a materialfor a specific purpose is a lengthy and expensive process. Approx-imately always more than one material is suitable for an applica-tion, and the final selection is a compromise that brings someadvantages as well as disadvantages [11]. Material selection is aninterdisciplinary effort and it often requires different fields of studysuch as material science and engineering, mechanical engineering,industrial engineering and other experts in the field of application.

Chiner [10] proposed five steps for material selection: definitionof design, analysis of material properties, screening of candidatematerials, evaluation and decision for optimal solution, and

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Materials and Design 31 (2010) 696–705

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verification tests. Farag [4] described the different stages of designand the related activities of the material selection. Farag definedthree stages of selection: namely initial screening, developingand comparing alternatives, and selecting the optimum solution.Change in a material will likely cause a change in the manufactur-ing process. According to Ashby et al. [12] achieving the matchwith design requirements involves four fundamental steps. (1) Away for translating design necessities into a requirement for mate-rial and process. (2) A method for screening out those that cannotmeet the specification, leaving a subset of the original menu. (3) Amethod for ranking the surviving materials and process, identify-ing those that have the best potential. (4) An approach of searchingfor supporting information about the top-ranked candidates, givingas much background information about their strengths, weak-nesses, history of use and future potential as possible. Jalham[13] proposed the preselecting, selection, post-selection and tech-no-economic phases in material selection. Moreover, Van Kesterenet al. [14] suggested basic materials selection activities as follow:formulating material criteria, making a set of candidate materials,comparing candidate materials and choosing candidate material. Itseems that there are similarities between steps expressed by Chin-er [10], Farag [4], Ashby et al. [12], Jalham [13] and Van Kesterenet al. [14]. In general, one of the issues that emerges from thesefindings is that regardless of the relation of design stages and pro-cess selection with material selection, screening and ranking aretwo vital steps in the material selection. A variety of quantitativeselection methods have been developed to analyze the materialselection process, thus a systematic evaluation can be made. Thispaper seeks to address the following questions:

RQ1: What is the contribution of the literature in the field ofscreening and choosing the materials?RQ2: What are the methodologies/systems/tools for materialselection of engineering components?RQ3: Which approaches were prevalently applied?RQ4: Is there any inadequacy of the approaches?

The objective of this paper is to review the research work donein the field of material selection. However, the scope of this reviewis limited to the literatures that suggest new system/tool/methodto assist decision makers in evaluating and selecting materials.The remainder of this paper is organized as follows, Sections 2and 3 classify material screening and ranking methods, respec-tively, according to the literature of research, so the two first re-search questions will be answered in these sections. Section 4analyses the trend of material selection researches, discusses themost prevalently used approaches, and finds out the advantagesand limitations of the methods. Therefore, Section 4 will addressquestions 3–4. Final section concludes with the inferences of theundertaken research.

2. Materials screening methods

After the alternatives have been proposed, the ideas which areclearly unsuitable are eliminated and the focused attention is onthose that seem practical. In the following section methods for ini-tial screening of materials according to literature are presented.Screening methods narrow down the choices to a manageablenumber for subsequent detailed evaluation [4]. Fig. 1 shows classi-fication of screening methods in material selection.

2.1. Cost per unit property method

Since cost is so important in selecting materials, it is logical toconsider it at the start of material selection process. Generally, a

target cost is set to remove the materials that are very expensive.So the final choice is a trade-off between cost and performance.It can be the most useful factor when it is related to a critical mate-rial property that controls the performance of the design. The costper unit property method is proper for initial screening in applica-tions where one property stands out as the most critical servicerequirement [15]. In this case, it is possible to estimate the costof providing most critical requirement for various materials.Cost/unit tensile ($$/MPa) strength is usually one of the mostimportant criteria and materials with lower cost/unit strengthare preferable. However the main limitation of this explanation isthat it considers only one property as the most critical and ignoresother properties.

2.2. Chart method

The material selection system developed by Ashby [16] concen-trates on the data modeling aspect of the problem by presentingthe data in a chart format. Ashby’s materials and process selectioncharts are a tried and tested materials selection method. Ashby’smaterial selection charts [16–18] are useful for initial screeningof materials. Cambridge Engineering Selector (CES) is a powerfulselection and analysis tool that is based on the Ashby’s materialsselection methodology. In the field of mechanical design thesecharts are a simple and quick way of assessing whether a materialis suitable for the case in hand [19].

Weaver et al. [20] used the index-and-chart method for mate-rial selection of refrigerator considering environmental impactindicators. Sepe [21] also illustrated some items which can beadded or replaced the current property chart. Chart method wasextended by Holloway [19] to take into account environmental fac-tors. The benefit of this approach is that it is systematic and unbi-ased in its focus on product objectives. Chart method is easy whenthe design of the component specifies a simple objective such asminimizing weight and a single constraint, for instance a specifiedstiffness, strength, or thermal conductance [22]. Perhaps the mostserious limitation of this method is that, the chart limits the deci-sions in material selection to only solving two or three criteria.Multi-criteria decision making for material choice is establishedto address the problem.

material screening methods

Cost per unit property method

Chart method

Questionnaire method

Materials in products selection

tools

Artificial intelligence

methods

Computer-aided materials selection

systems

Case-based reasoning

Knowledge –based systems

Neural networks

Fig. 1. Classification of screening methods in material selection.

A. Jahan et al. / Materials and Design 31 (2010) 696–705 697


2.3. Questionnaire method

Questionnaire method has been suggested by a number ofresearchers. Farag [15] classified performance requirements ofmaterial into two main categories: Rigid, or go–no-go require-ments and soft, or relative requirements. Rigid requirementsshould be met by the material if it is to be considered at all. Suchrequirements can be used for the initial selection of materials toeliminate the unsuitable groups. For instance, metallic materialsare eliminated when selecting materials for an electrical insulator.Soft, or relative, requirements are subject to negotiation and trade-offs. Edwards [23] provided set of questions to improve the likeli-hood of achieving an optimal design solution. In the followingsome of them are mentioned:

� Have all the related discrete material properties been obtainedand understood?

� Have all the environmental conditions been taken intoconsideration?

� Have all the economic limitations been taken intoconsideration?

� Will the design conditions change with time?� Have the effects of materials processing conditions been

considered?� Have the effects of quantities and rate or production of compo-

nents been effectively considered?� Has future raw material accessibility been considered?

Moreover, Van Kesteren et al. [6] proposed question tool foruser-interaction aspect of material. The tool is composed of a listof questions for different phases in the user-product interactionand a checklist of sensorial properties. The product designer andthe client imagine and talk about the interaction that the user willhave with a new product in phases of first contact, try out, trans-port, unwrapping, usage, and rest phase. In addition, Pedgley [24]studied on influences of clients, manufacturers/vendors, usersand designers on the selection of materials and processes in indus-trial design. The aforementioned author also described howdesigners manage influences of these stakeholders and coordinatetheir integration into selection activities. In their studies, theinfluences characterized as follows: strategic/commercial (clients),feasible/obtainable (manufacturers and vendors), perceptual/experiential (users) and circumstantial/personal (designersthemselves).

2.4. Materials in products selection tools

Van Kesteren et al. [6] proposed the Materials in ProductsSelection (MiPS) tools, which are new tools for incorporatinguser-interaction aspects into the materials selection process.These tools help clients in clearly specifying requirements that re-late to user-interaction aspects and form consensus betweenthem and product designer in early stages of materials selection[7]. Preferably the tools do not direct to material names, becausesome materials may not be able to fulfill project objectives interms of technical properties. Furthermore, new materials areeasily excluded when they are defined at the beginning of a pro-ject. MiPS consists of picture tool (pictures of product examplesand their materials), sample tool (actual materials samples) andquestion tool (sensorial aspects of materials during several phasesof the user-product interaction). To optimize the tools Van Kest-eren et al. [7] also suggested emphasizing further developmentson the converging step of the tools. Aforementioned authors’themselves mentioned that the tools only able to translate alow percentage of user-interaction aspects into sensorialproperties.

2.5. Artificial intelligence methods

Artificial intelligence is the study of how to make computersperform intelligent things for the processing of unstructured scat-tered knowledge for the solution of complex problems. One ofthese problems is the selection of materials. No engineer expectsto know more than a small division of this ever-growing body ofinformation in material selection. At one time, an engineer couldrely upon engineering handbooks, journals and his experience toselect the appropriate material for an application but nowadaysengineers are forced to look for systematic techniques for manag-ing and analyzing engineering data on the growing array of mate-rials [3].

2.5.1. Computer-aided materials selection systemsIt is a self-evident need for information management systems

due to large number of materials in the world [12]. Hanley [25] de-scribed two analytical approaches for computer-aided selection ofengineering materials. Dargie et al. [26] presented a computer-aided design system for suggesting candidate manufacturing pro-cess and material combinations. Furthermore, Lai and Wilson[27] suggested interactive computer program and artificial intelli-gence techniques to select candidate material and primary processcombinations for a part, during the early stage of design. For eachcombination, secondary process chains was generated and opti-mized according to a minimum cost criterion. Moreover, Pechtet al. [28] also developed a decision support program called mate-rial selection program (MSP) for material selection processes. Theprogram utilizes simple decision support techniques and userspecified constraints to select a list of materials from a data basecontaining information on a variety of material attributes. MSPsearches a database and produces a ranked list of materials basedon weighted search criteria. Sawyer and Pecht [29] presented amicrocomputer-based that aids material selection and discussedthat choice of materials generally depends on four broad materialcategories: function, appearance, manufacture, and cost. Moreover,Ullman and Ryden [30] developed a national materials data base inSweden. Darmody and Chadwick [31] proposed an expert systemto assist engineers in selecting adhesives for aerospace hardwaredesign. Chen et al. [32] also presented an approach and computerprogram for material selection which mixes the environmental lifecycle impacts of materials into traditional engineering materialselection process. Cebon and Ashby [33] argued for key require-ments of software and data systems for organizing materials infor-mation and for optimal selection. Jalham [13] proposed a decision-making integrated information technology approach for materialsselection. Kumar and Singh [34] offered an intelligent system formodeling and material selection for progressive die components.However, these systems are appropriate tools for searching andnot for calculations. Although some of the material databases canalso be used as material selection systems, they are essentiallydeveloped for data storage and the material selection process usingthem is not very reliable [35].

2.5.2. Knowledge-based systemsKnowledge-based systems (KBS) are systems based on the

methods and techniques of artificial intelligence and is also calledexpert system. KBS is defined as a computer system that is plannedto imitate human problem-solving by means of artificial intelli-gence and reference to a database of knowledge on a particularsubject. During last decades information on engineering materialspresented in two categories: data and knowledge, the distinctionbetween a knowledge base and a database is still unclear [36].‘Data’ are defined as the result of measurements that can be pre-sented in numbers, whereas ‘knowledge’ represents the connec-tions between items of data and is mostly expressed in plain

698 A. Jahan et al. / Materials and Design 31 (2010) 696–705


language. According to these categories, there are two possibilitiesto maintain the process of material selection, via material dat-abases or knowledge-based systems. Computerized database arethe best form of presenting the former, because of providing easyaccess to the materials’ data. The latter comprise expert knowledgecapable of assisting the user in an interactive way to solve differentproblems and queries [37]. The knowledge-based system works infull interactive mode [38] and provide impartial recommendationsand are able to search large databases for optimum solutions [4].Bamkin and Piearcey [39] justified the development of a ‘DesignAssistant’ program for the selection of materials according toknowledge-based system. Bullinger et al. [40] developed a knowl-edge-based system for assisting design engineers in selecting theappropriate materials for construction with fiber-reinforcedcomposite materials. Furthermore, Chen et al. [41] integrated theexpert system with the database system to provide decision-mak-ing support system for composite material selection in structuraldesign. Sapuan and Abdalla [42] also presented a prototype knowl-edge-based system for selection of polymeric-based compositematerial for pedal box system of automotive. Sapuan [35] ex-plained the importance of knowledge-based system in thecontext of concurrent engineering and applied it in material selec-tion of polymeric-based composite. Later, Sapuan et al. [38] dem-onstrated application of knowledge-based system in materialselection of ceramic matrix composites for engine components.Moreover, Zha [43] described the work of selecting suitable manu-facturing processes and materials in concurrent design accordingto a fuzzy knowledge based decision support method. Based onthe proposed method, a prototype web-based knowledge-intensivemanufacturing consulting service system with the client-knowl-edge server architecture was developed to help designers/usersfind good processes and materials while still at the conceptuallevel of design. Meanwhile Kumar and Singh [44] presented anintelligent system for selection of materials for progressive diecomponents.

2.5.3. Case-based reasoningAmen and Vomacka [45] used case-based reasoning (CBR) as a

tool for material selection. CBR is the procedure of solving newproblems based on the solutions of similar past problems. CBR isa good technique for searching in databases with information ofdifferent technical solutions applied in the actual company, failureanalysis, and so on. Meanwhile it can easily be updated. CBR doesnot require an explicit domain model and so evocation becomes atask of gathering case histories, implementation is reduced to iden-tifying significant features that describe a case, an easier task thancreating an explicit model. By applying database approach largelyvolumes of information can be managed, and CBR systems canlearn by getting new knowledge as cases thus making maintenanceeasier. Critics of CBR discuss that it is an approach that acceptsanecdotal evidence as its main operating principle. Without statis-tically relevant data for supporting and implicit generalization,there is no guarantee that the generalization is correct.

2.5.4. Neural networksIf all knowledge about material is stored in an expert network

instead of manually using handbooks, thumb-rules and heuristicsthen the selection of material will become easier. Goel and Chen[46] showed application of expert network in material selectionin the following steps: making a list of applicable materials by neu-ral network and expert system and arrange them in order of close-ness to the desired properties, then checking whether the materialis available in the inventory of the company or obtainable in thedesired time and analyzing the costs using expert system. In Goeland Chen research the neural network proved to be useful inselecting the best alternative from the database, but neural net-

work alone is not sufficient, because in some cases it may not beable to provide a unique solution. Beside, Li et al. [47] showedapplication of artificial neural networks in gear material selection.Amoiralis et al. [48] also used neural network for the selection ofwinding material in power transformers. Moreover, Balakrishnaet al. [49] demonstrated the materials selection processes in theenvironment of structural design of underwater vehicle propulsorrotor blade using neural networks.

3. Materials comparing and choosing methods

The selection of the suitable material is a difficult process thatdemands the management of a great amount of information aboutthe materials properties and there are often several solutions for aparticular application [10]. After narrowing down the field of pos-sible materials via one or more of the initial screening methods de-scribed in the last section, ranking methods can be used to furthernarrow the field of possible materials to a few optimum candi-dates. In literature, multi-criteria decision making (MCDM) meth-ods and optimization approaches have been applied in thisregard. MCDM methods divide to two main groups, multiple objec-tive decision making (MODM) and multiple attribute decisionmaking (MADM) approaches. There are several methods in eachof the above categories. Each technique has its own characteristicsand the methods can combine with each other or fuzzy methods.Fig. 2 shows classification of material choosing methods found inthe literature.

3.1. Multiple attribute decision making approaches

In contemporary researches, the performance of materials isevaluated against multiple criteria rather than considering a singlefactor. The selection of an optimal material for an engineering de-sign or manufacturing process from among two or more alterna-tive materials on the basis of two or more attributes is a multipleattribute decision making (MADM) problem [50]. The decisionvariables can be quantitative or qualitative. A number of theseattributes can be expressed as numbers, like density or thermalconductivity; some are boolean, such as the ability to be recycled;some, like resistance to corrosion, can be expressed only as a rank-ing (poor, adequate, good, for instance); and some can only be cap-tured in text and images [12].

3.1.1. TOPSISSharma et al. [51] proposed an expert system based on Tech-

nique of ranking Preferences by Similarity to the Ideal Solution(TOPSIS) for aid in material selection process. Jee and Kang [52]introduced hybrid of entropy and TOPSIS as tool in computer aidedengineering (CAE) to help design engineers for material selection.As an example, the procedure of optimal material selection for aflywheel has been developed in their work. Milani [53] applied en-tropy and TOPSIS in gear material selection and studied on effect ofnormalization norms on ranking of materials. Shanian and Sava-dogo [54] showed application of TOPSIS as a MADM method forsolving the material selection problem of metallic bipolar platesfor polymer electrolyte fuel cell. Since the entropy method fordeciding the relative importance of attributes does not give scopeto designer’s preferences, in their study a revised entropy methodwas used for calculation of relative importance of each criterion.They also compared ordinary TOPSIS method to a modified versionand showed efficiency of proposed method. Huang et al. [55] usedthe possible solutions search algorithm (PSSA) to pre-select thematerials to obtain the feasible solutions, and applied TOPSISmethod to acquire the optimal solution. Rao and Davim [50] of-fered a decision-making framework model for material selection



using a combined multiple attribute decision-making method. Theprocedure was based on a combined TOPSIS and AHP method.

3.1.2. ELECTREShanian and Savadogo [56] used method of ELECTRE IV for

material selection of the bipolar plate of a polymer electrolyte fuelcell as a non-compensatory compromised solution. They pro-ceeded to make lists of candidate materials from best to worstby taking into account all the material selection criteria. Shanianand Savadogo [57] explained ELECTRE I and used it in materialselection of bipolar plates in Polymer Electrolyte Fuel Cells. Sha-nian and Savadogo [58] also presented a compressive descriptionof the ELECTRE and the principals of outranking approach. Theyused entropy weights and applied ELECTRE method for materialselection of mass produced non-heat-treatable cylindrical covermaterial. Milani and Shanian [59] applied ELECTRE III for Gearmaterial selection with uncertain and incomplete data. Shanianand Savadogo [60] also offered a methodological concept for mate-rial selection of highly sensitive components based on multiple cri-teria decision analysis. They discussed about different version ofELECTRE and studied on sensitivity to ranking of VIKOR [61], twoversions of TOPSIS as a compensatory methods, and ELECTRE ISand ELECTRE IV as a non-compensatory methods. Shanian et al.[62] also showed application of ELECTRE III for group materialselection under weighting uncertainty. They described ELECTREIII, as a non-compensatory method and emphasized on importanceof group decision making in material selection.

3.1.3. Analytical hierarchy process (AHP)Individual and integrated AHP approaches in decision making

are very prevalent. The wide applicability is due to its simplicity,ease of use, and great flexibility. Cao et al. [63] developed an algo-

rithm according to analytic hierarchy process (AHP) and grey rela-tion analysis (GRA) for material selection. They emphasized onenvironmental factors and tested the method in material selectionof the housing of an electronic device. Dweiri and Al-Oqla [64] alsoapplied AHP and performed sensitivity analysis to increase theconfidence in the choice of material. Rao and Davim [50] and Rao[2] used AHP to find out the relative importance of different attri-butes with respect to the objective in material selection.

3.1.4. Simple additive weighting methodFarag [15] proposed a simple mathematics-based weighted

properties method that can be used when several propertiesshould be taken into consideration. In his approach, relative impor-tance of criteria was calculated by digital logic (DL) method. In DLmethod as the number of alternatives increases, the amount of cal-culations rises quite rapidly and computational procedures arequite elaborate. A weighted-property value was obtained by multi-plying the numerical value of the property by the weighting factor.Chiner [10] applied weighting method in his suggested steps formaterial selection. Moreover, Wade [65] compared the parametersof various dielectric materials based on weighted average method.Furthermore, Quigley [66] applied weighted materials propertyselection procedure in compliant-layer artificial hip joints. Jalham[67] also discussed about application of quality function deploy-ment (QFD) method in material selection and claimed that thismethod can substitute the weighted properties method, whichhas a misleading approach and lacks the systematic sequence. Fur-thermore, Dehghan-Manshadi et al. [68] developed a new methodfor calculating relative importance of criteria (weights) in simpleadditive weighting method (SAW) or weighted properties method(WPM). They modified DL approach and integrated it with non-lin-ear normalization, then examined proposed method with two case

Fig. 2. Classification of material choosing methods.



studies and compared results with classical weighted propertymethod. They concluded that the new approach is capable of pro-viding more reasonable selections as opposed to those obtainedfrom the classical weighted method. Moreover, Fayazbakhshet al. [69] proposed Z transformation in SAW for dimensionlessof decision matrix to overcome the shortcoming of modified digitallogic method.

3.1.5. Limits on properties methodIn the method described by Frag [15], the performance require-

ments are divided into three categories: lower limit properties;upper limit properties and target value properties. The limits canbe used for eliminating unsuitable materials from a data bank.After the elimination stage, the limits on properties method [15]can be used to optimize the selection among the remainingmaterials.

3.1.6. Individual methods in MADMRao [70] used graph theory and matrix approach for material

selection. Since any number of quantitative and qualitative factorscan be considered in this method, it enables a more critical analysisthan the DL method. However, the method does not have a condi-tion for checking the consistency made in the judgments of relativeimportance of the attributes. Further, the method may be difficultto deal with if the number of attributes is more than 20. Bovea andGallardo [71] demonstrated the need to perform a sensitivity anal-ysis when a single environmental score is applied during the pro-cess of selecting materials. Using grey relational analysis, themulti-criteria weighted average is proposed by Chan [72] in thedecision-making process in order to rank the material options un-der the conditions of non-linear constraints, uncertainties and con-flicting objectives. Chan and Tong [73] proposed a multi-criteriaweighted average method using grey relational analysis to rankthe materials considering environmental factors. Furthermore,Rao [2] improved the compromise ranking method or VIKOR(VIšekriterijumsko KOmpromisno Rangiranje) method by intro-ducing analytic hierarchy process (AHP) for assigning the weightsof relative importance of attributes and by introducing a rankedvalue judgment on a fuzzy conversion scale for the qualitative val-ues of attributes. VIKOR is a helpful tool particularly in a situationwhere the decision maker is not able, or does not know how to ex-press preference at the beginning of decision making procedure.

3.2. Fuzzy multi-criteria decision-making methods

For material properties like corrosion and wear resistance,machinability and weldability, numerical values are rarely givenand materials are usually rated as very good, good, fair, poor, etc.Meanwhile, the characteristics of different materials stated in theengineering design handbooks (e.g. Bralla [74] and Boyes [75])are multi-dimensional and qualitative. For example, some of theimportant attributes of tool steels are non deforming properties,safety in hardening, toughness, wear resistance and machine abil-ity. The assessments of these attributes by experts are given in lin-guistic terms such as, poor, fair, good, excellent, etc. [76]. Materialproperties are also of varying degrees of significance for differentdesign requirements. The desired value and importance weight ofa material characteristic are usually described in a linguistic fash-ion. For instance, it is ‘‘very important” that the corrosion resis-tance property of the selected material must be ‘‘recommended”for it to work in the specified corrosive environment. Or, it is‘‘important” that raw material price should be ‘‘smaller or equalto a certain value.” It is not easy to precisely quantify the ratingof each alternative material [77]. It seems that the final choice ofmaterial faces to a risk because of incomplete, approximate andpossibly incorrect information [23]. In such cases, fuzzy logic can

be very useful. Fuzzy set theory was developed exactly based onthe idea that the key elements in human thinking are not numbers,but linguistic terms or labels of fuzzy sets. Application of fuzzy lo-gic in material and process selection also would be useful, becausethese decisions are made during the preliminary design stages inan environment characterized by imprecise and uncertain require-ments, parameters, and relationships [78].

Thurston and Carnahan [79] recommended to use fuzzy analy-sis in the earliest stages of preliminary design evaluation or in sit-uations limited to semantic input from design decision makers.Wang and Chang [76] also proposed a fuzzy set multiple criteriadecision making approach for tool steel materials selection. Theimportant weights of different criteria and the material adequacyratings of various alternatives under different criteria were givenin linguistic terms. They used a hypothetical example to describetheir fuzzy group multiple criteria decision making approach. Afuzzy multi-criteria decision-making method for material selectionpresented by Liao [77]. Chen [80] proposed a method that usessimple arithmetic operations rather than the complicated arithme-tic operations for aggregation and ranking of fuzzy numbers de-scribed by Wang and Chang [76]. Moreover, Giachetti [78]integrates a formal multi-attribute decision model with a rela-tional database for material and manufacturing process selection.The values of material properties are generally qualitatively de-scribed or imprecisely measured using ranges [77], so Giachettiused possibility theory and fuzzy set. The theoretical foundationof his method was based on the assessment of compatibility rat-ings between the product profile requirements and each alterna-tive. Sarfaraz Khabbaz, et al. [81] also used a simplified fuzzy insimultaneous dealing with quantitative and qualitative materialproperties.

3.3. Multiple objective decision making approaches

The material designer should then make a compromise amongthe objectives to come up with the best solution. Unlike the exactsciences, where there is usually only one single correct solution toa problem, materials selection and substitution decisions requirethe consideration of conflicting advantages and limitations, neces-sitating compromises and trade-offs; as a consequence, differentsatisfactory solutions are possible [4]. In designing the component,the designer has a purpose; to make it as cheap as possible, or aslight, or as safe, perhaps. This must be achieved subject to con-straints: that the component can carry the given loads without fail-ure, that certain dimensions are fixed, and that its cost is withincertain limits [20].

3.3.1. Multi-attribute utility analysisMulti-attribute utility analysis (MAUA) models are mathemati-

cal tools for evaluating and comparing alternatives to assist indecision making about complex alternatives. The models let youto assign scores to alternative choices in a decision situation wherethe alternatives can be identified and analyzed. The models arebased on the supposition that the apparent desirability of a partic-ular alternative depends on how its attributes were viewed. Amapping of a multidimensional attribute space into a single-dimensioned preference space is utility function. The method usesquestionnaire for measuring utility, but the interview processtakes too long to be feasible for most situations; also the inter-viewer should be very expert. Trade-off methods using utility func-tions allow optimal solutions to be found for two objectives, but forthree it is harder. Thurston and Carnahan [79] analyzed the prob-lem of preliminary material selection for an automotive bumperbeam to illustrate the application of fuzzy set analysis and multi-attribute utility analysis. Roth et al. [3] used MAUA as a tool formaterial selection. Aforementioned authors applied subjective



probability assessment (SPA) as an operations research techniqueto measure confidence of the decision maker in the levels of attri-butes for which there is a high degree of uncertainty. Then theseprobability distributions used in conjunction with MAUA to pro-vide a consistent framework for making materials selection deci-sions. Moreover, Sirisalee et al. [82] developed a method inMAUA for dealing with three objectives.

3.3.2. Goal programmingGoal programming (GP) is modification of linear programming

(LP), while LP deals with only one single objective to be minimizedor maximized, and subject to some constraint. GP, instead, can beused as an effective method to handle a decision concerning multi-ple and conflicting goals. Meanwhile, the objective function of agoal programming model may consist in non-homogeneous unitsof measure. Schniederjans et al. [83] applied goal programming ap-proach for energetic material evaluation decisions.

3.3.3. Genetic algorithm and neural networkSmith et al. [84] showed by adapting the customer’s designs to

the process requirements, neural network can offer good input set-tings for the process in order to achieve required mechanical orphysical properties of the final material. Metal powder compactionand sintering are in fact complex processes, where many mecha-nisms can operate concurrently to influence the final propertiesof the material. In comparison to linear regression, the networkwas found to provide a 36% reduction in the standard deviationof the inverse solutions and consequently caused reduction of timeand cost. Yang et al. [85] used neural network and genetic algo-rithms for the selection of optimum composite material and oper-ating conditions. Jarupan et al. [86] presented a genetic algorithmbased approach for addressing the packaging material selectionproblem through multiple criteria decision-making. Further intheir study, fuzzy set theory was used for representing and manip-ulating the vague and subjective descriptions of packaging perfor-mance and design attributes. Moreover, Shi [87] developed back-propagation neural network for plastic material selection. The rela-tionship between performance requirements of injection moldedpart and properties of plastic material were analyzed and thenthe selection of the material was represented by a fuzzy mathe-matics method. Furthermore, Cui et al. [88] for optimal design ofautomotive body assembly formulated the design problem as amulti-objective non-linear mathematical programming problemwith discrete and continuous variables. The discrete variables werethe material types and continuous variables were the thicknessesof the panels and the problem was solved using a multi-objectivegenetic algorithm. An artificial neural network was employed toapproximate the constraint functions and reduce the number of fi-nite element runs. Zhou et al. [89] applied artificial neural net-works and genetic algorithm approach to optimize multiobjective material selection of drink containers. They formulatedobjectives of: reducing the weight of the container, minimizingthe cost of the container and minimizing environmental pollutionby considering seven materials as discrete variables.

3.3.4. Individual methods in MODMThe mini–max dual method is applied by Imai [90] for solving

the material selection structural optimization problems. In thismethod general objective function was introduced to considerstructural weight and material cost simultaneously. Fenton andGagnon [91] proposed a computer based methodology for cut-ting-tool material selection. They used numerical optimizationfor solving the multi-objective optimization problem (unit produc-tion time vs. unit cost). Ashby [92] suggested multi-objective opti-mization method that are equally applicable to material selectionand to material design. According to Ashby [92], the goal of choos-

ing a material is to optimize a number of metrics of performance inthe product in which this material is used. Common among thesemetrics are cost, mass, volume, power-to-weight ratio, energy den-sity, and there are many more other characteristics that can be inconflict with each other. Giudice et al. [93] used multi-objectivetechniques in the Life-Cycle Design process for materials selectionof components considering environmental aspects.

3.4. Optimization methods

Extensive optimization approaches have been proposed formaterial selection, such as mathematical programming, computersimulation, and genetic algorithm.

3.4.1. Mathematical programmingGero [94] reviewed application of operations research princi-

ples to design of the structures and selection of materials. More-over, Farag and El-Magd [95] used benefit-cost analysis to selectthe optimum design-material-process combination. Chen et al.[96] also offered an economic model for raw material selectionwhich considered two decision factors: initial raw material costand additional manufacturing costs including extra operator cost,extra machine cost and extra quality cost that may occur due toinappropriateness of raw material quality. Furthermore, Sepulveda[97] presented a methodology for structural synthesis based onbranch and bound for optimal material selection of truss struc-tures. Structural member sizes and material selection variables interms of (0, 1) variables were treated simultaneously as designvariables. Eliasson and Sandstrom [98] applied material selectionand grade optimization in aluminum matrix composites. The influ-ence of matrix material, amount of reinforcement, and value ofweight savings was studied by them. Stolpe and Svanberg [99] ap-plied linear programming for material selection and determiningthe area of each bar in a truss structure in order to minimize costof structure and subject to stress constraints under a single loadcondition.

3.4.2. Computer simulationIntegrating material property database with design algorithms

and computer-aided design (CAD)/computer-aided manufacturing(CAM) programs has a lot of advantages including homogenizationand sharing of data in the different departments, decreased redun-dancy of effort, and decreased cost of information storage and re-trieval [4]. Goldsberry [100] discussed about application ofcomputer simulation in material selection. They used Moldflowsoftware to evaluate various materials of different costs. Flow anal-ysis is the ability to simulate one material vs. another rather thandoing trial and error in a molding machine. Meanwhile rapid ad-vances in finite element (FE) methods have allowed applicationof computer simulation as part of an optimization strategy for de-sign of composite structures. According to Aceveset al. [101], theseapproaches are valuable where a single objective can be definedand they are less useful where there are conflicting in design objec-tives (e.g. minimum weight and cost).

3.4.3. Genetic algorithmBecause of wide variety of material combinations, reinforce-

ment geometries and architectures in the engineering design ofcomposites, Sadagopan and Pitchumanit [102] used genetic algo-rithm to optimal design of composite materials and compared re-sults with alternative approach based on the simulated annealingtechnique reported previously by them. Zhang [103] used GeneticAlgorithm for selecting optimal material constituent compositionsand microstructures according to the determined or optimizedmaterial properties.



4. Observations

The distribution, application areas and the year of application ispresented in Table 1 to highlight the research trend about materialscreening and choosing methods. It is observed that there is agrowth in the study of the material evaluation and selection prob-lem using the multi-criteria decision making approaches from

2006. What is interesting in this data is that, more than half of re-searches (52%) are about application of MCDM methods. It is ex-pected that the number will keep increasing in the coming years,because in material selection there are often several solutions fora particular application and materials affect many aspects of aproduct, firm-architecture, manufacture, and product performance,so more precise approach would be required.

Table 1Trend of research in material screening and choosing methods.

Material screening and selection methods Before 2000 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 Total

Screening methodsCost per unit property method 1 1 36Chart method 5 1 6Questionnaire method 1 1 1 3Materials in products selection tools 2 2Computer-aided materials selection systems 8 1 1 1 11Knowledge-based systems 4 1 1 1 1 8Cased-based reasoning 1 1Neural networks 1 1 1 1 4

Choosing methodsMCDM

TOPSIS 1 1 1 2 1 6 49ELECTRE 4 1 1 6AHP 2 2 4SAW 3 1 1 1 1 7Limits on properties method 1 1Individual methods in MADM 3 1 1 5Fuzzy MCDM 5 1 6Multi-attribute utility analysis 2 1 3Goal programming 1 1GA and neural network 1 1 1 1 1 1 6Individual methods in MODM 2 1 1 4

OptimizationMathematical programming 5 1 6 10Computer simulation 1 1 2Genetic algorithm 1 1 2

Total 40 2 3 3 2 4 5 15 9 8 4 95

Table 2Advantages and limitation of screening methods in material selection.

Name of the method Advantages Limitation

Cost per unit propertymethod

� The final choice is a trade-off between cost and critical material propertythat controls the performance of the design

� It considers only one property as the most critical andignoring other properties

Chart � Very useful for initial screening of material in phase of mechanicaldesign

� Limits the decisions system for material selection toonly two or three criteria

Questionnaire method � Improve the chance of achieving an optimal design solution � It cannot offer any ranking

Materials in productsselection tools

� Assist clients in clearly specifying requirements that relate to user-inter-action and form consensus between them and product designer aboutthese aspects in early stages of materials selection

� The tools only able to translate a low percentage ofuser-interaction aspects into sensorial properties

Computer-aided materialsselection systems

� These systems are appropriate tools for searching � Database systems provide a lot of data and informa-tion but provide very little decision-making support

� Material selection procedure using a material databaseis not very reliable [35]

Knowledge-based systems � Expert systems offer impartial recommendations and are able to searchlarge databases for optimum solutions

� Another important benefit of expert systems is their ability to capturevaluable expertise and make it available to a wider circle of users [4]

� Knowledge elicitation is a difficult process and it is noteasy to maintain this system

� These systems are not suitable tools for ranking

Case-based reasoning � CBR is a good technique for searching in databases with information ofdifferent technical solutions applied in the actual company, failure anal-ysis, and so on and can easily be updated

� CBR systems can learn by getting new knowledge as cases thus makingmaintenance easier

� Critics of CBR debate that it is an approach that acceptsanecdotal evidence a without statistically relevantdata for backing and implicit generalization, there isno guarantee that the generalization is correct

Neural network � Neural network can to be useful in selection of the best alternative fromthe database

� Neural network alone is not sufficient, because insome cases it may not be able to provide a uniquesolution [46]



The third objective (RQ3) of this paper is to find out the mostpopular approach adopted in the literature of material evaluationand selection. According to Table 1, TOPSIS, ELECTRE and AHP havebeen the most popular state of the art methods in material choos-ing. Chart method, Computer-aided materials selection and knowl-edge-based systems are the most prevalent approach in materialscreening. Fuzzy methods prevalently have been used either indi-vidually or with other methods such as genetic algorithm [86],neural networks [87], KBS [43] and MCDM methods [2,50,70,79].

The last objective (RQ4) of this paper is to critically analyze theapproaches and try to find out some drawbacks. Table 2 arguesabout advantages and limitations of screening methods in materialselection. Instead of analyzing every single approach in materialchoosing methods, the main focus of this section is confined toTOPSIS, ELECTRE and AHP, which are the three most popular selec-tion approaches after 2005. Although ELECTRE methods have goodoutput, they have a number of limitations:

� As the number of alternatives increases, the amount of calcula-tions increases quite rapidly and computational procedures arequite elaborate.

� ELECTRE only determine the rank of each material and do notgive numerical value for better understanding of differencesbetween alternatives.

AHP is a powerful and flexible decision making procedure tohelp one set priorities and make the best decision when both tan-gible and non tangible aspects of a decision need to be considered,but it only can compare a very limited number of decision alterna-tives, which is usually not more than 15. When there are hundredsor thousands of option to be compared, the pair wise comparisonmanner provided by the traditional AHP is obviously infeasible.

TOPSIS is a good choice for material selection because of follow-ing reasons:

� It is useful for qualitative and quantitative data.� It is relatively easy and fast, with a systematic process.� The output can be a preferential ranking of the candidate mate-

rials with a numerical value that provides a better understand-ing of differences and similarities among alternatives.

� This is especially useful when dealing with a large number ofalternatives and criteria; the methods are completely suitablefor linking with computer databases dealing with materialselection.

5. Conclusion

First, it was found that numerous individual and integrated ap-proaches were proposed to solve the material selection problem.The classification of the literature must be of value both to otherresearchers who are interested in the state of the art of materialevaluation and selection methods, and to engineers who needinformation about how to select a material for an application.The traditional cost-based approach or chart method cannot guar-antee that the selected material is the best because they limit thedecisions in material selection to only two or three criteria. This re-search not only provides evidence that the multi-criteria decisionmaking approaches has the potential to greatly improve the mate-rial selection methodology, but also aids the researchers and deci-sion makers in applying the approaches effectively.

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Material screening and choosing methods – A review

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