Materials selection is a matter of great importance to engineering design and software tools are valuable to inform decisions in the early stages of product development. However, when a set of alternative materials is available for the different parts a product is made of, the question of what optimal material mix to choose for a group of parts is not trivial. The engineer/designer therefore goes about this in a part-by-part procedure. Optimizing each part per se can lead to a global sub-optimal solution from the product point of view. An optimization procedure to deal with products with multiple parts, each with discrete design variables, and able to determine the optimal solution assuming different objectives is therefore needed. To solve this multiobjective optimization problem, a new routine based on Direct MultiSearch (DMS) algorithm is created. Results from the Pareto front can help the designer to align his/hers materials selection for a complete set of materials with product attribute objectives, depending on the relative importance of each objective.
This is a preview of subscription content, log in to check access.
Buy single article
Instant access to the full article PDF.
Price includes VAT for USA
Subscribe to journal
Immediate online access to all issues from 2019. Subscription will auto renew annually.
This is the net price. Taxes to be calculated in checkout.
ArcelorMittal (2015) ArcelorMittal automotive steel solutions. http://goo.gl/N0nmXi
Ashby MF (2000) Multi-objective optimization in material design and selection. Acta Mater 48(1):359–369
Ashby MF (2005) Materials selection in mechanical design. MRS Bull 30:995
Bar-On I, Kirchain R, Roth R (2002) Technical cost analysis for pem fuel cells. J Power Sources 109(1):71–75
Custodio AL, Madeira JFA, Vaz AIF, Vicente LN (2011) Direct multisearch for multiobjective optimization. SIAM J Optim 21(3):1109–1140
Dewhurst P, Boothroyd G (1988) Early cost estimating in product design. J Manuf Syst 7(3):183–191
Elgh F, Cederfeldt M (2008) Cost-based producibility assessment: Analysis and synthesis approaches through design automation. J Eng Des 19(2):113–130
Ermolaeva NS, Castro MB, Kandachar PV (2004) Materials selection for an automotive structure by integrating structural optimization with environmental impact assessment. Mater Des 25(8):689– 698
Esawi AMK, Ashby MF (2003) Cost estimates to guide pre-selection of processes. Mater Des 24(8):605–616
Field F, Kirchain R, Roth R (2007) Process cost modeling: Strategic engineering and economic evaluation of materials technologies. JOM 59(10):21–32
Fixson SK (2005) Product architecture assessment: A tool to link product, process, and supply chain design decisions. J Oper Manag 23(3-4, Special Issue: Coordinating product design, process design, and supply chain design decisions):345–369
Fuchs ERH, Bruce EJ, Ram RJ, Kirchain RE (2006) Process-based cost modeling of photonics manufacture: The cost competitiveness of monolithic integration of a 1550-nm dfb laser and an electroabsorptive modulator on an inp platform. J Lightwave Technol 24(8):3175
Fuchs ERH, Field FR, Roth R, Kirchain RE (2008) Strategic materials selection in the automobile body: Economic opportunities for polymer composite design. Compos Sci Technol 68(9):1989–2002
ASM Handbook (1997) Materials selection and design, Ed. Dieter, EG ASM. Materials Park 998:OI-I
Johnson M, Kirchain R (2009) Quantifying the effects of parts consolidation and development costs on material selection decisions: A process-based costing approach. Int J Prod Econ 119(1):174–186
Kirchain RE (2001) Cost modeling of materials and manufacturing processes. Elsevier, Oxford, pp 1718–1727
Leite M (2015) Data for this paper. https://sites.google.com/site/smo2015dmsandms/
Leite M (2012) Techno economic evaluation in materials selection for multiple parts under oem-tier relations. Ph.D. thesis, Instituto Superior Técnico - Universidade Técnica de Lisboa. http://goo.gl/xqqj0w
Leite M, Silva A, Henriques E (2014) On the influence of material selection decisions on second order cost factors, chap. 4. Springer, London, pp 59–79
Luo A (2002) Magnesium: Current and potential automotive applications. JOM 54(2):42–48
Miettinen K (1999) Nonlinear multiobjective optimization. Kluwer Academic Publishers, Boston
Newnes LB, Mileham AR, Cheung WM, Marsh R, Lanham JD, Saravi ME, Bradbery RW (2008) Predicting the whole-life cost of a product at the conceptual design stage. J Eng Des 19(2):99–112
Niazi A, Dai JS, Balabani S, Seneviratne L (2006) Product cost estimation: Technique classification and methodology review. J Manuf Sci Eng 128(2):563–575
Olson GB (1997) Computational design of hierarchically structured materials. Science 277(5330):1237–1242
Rao SS (1996) Engineering optimization: Theory and practice. Wiley
Roth R, Clark J, Kelkar A (2001) Automobile bodies: Can aluminum be an economical alternative to steel JOM 53(8):28–32
Sapuan S (2001) A knowledge-based system for materials selection in mechanical engineering design. Mater Des 22(8):687–695
Schubel PJ (2010) Technical cost modelling for a generic 45-m wind turbine blade producedby vacuum infusion (vi). Renew Energy 35(1):183–189
Schubel PJ (2012) Cost modelling in polymer composite applications: Case study analysis of existing and automated manufacturing processes for a large wind turbine blade. Compos B Eng 43(3):953–960
ThyseenKrupp (2015) ThyseenKrupp automotive steel. http://goo.gl/VRfmb0
Weck OLde, Jones MB (2006) Isoperformance: Analysis and design of complex systems with desired outcomes. Syst Eng 9(1):45–61
Zhou CC, Yin GF, Hu XB (2009) Multi-objective optimization of material selection for sustainable products: Artificial neural networks and genetic algorithm approach. Mater Des 30(4):1209–1215
The authors thanks the financial support of FCT, Portugal, for financing the work under the MIT-Portugal Program. This work was supported by FCT, through IDMEC, under LAETA, project UID/EMS/50022/2013.
About this article
Cite this article
Leite, M., Silva, A., Henriques, E. et al. Materials selection for a set of multiple parts considering manufacturing costs and weight reduction with structural isoperformance using direct multisearch optimization. Struct Multidisc Optim 52, 635–644 (2015). https://doi.org/10.1007/s00158-015-1247-7
- Materials selection
- Multiple parts
- Automotive case study
- Direct search
- Multi-objective optimization