Optimal Lightweight Material Selection for Automobile Applications Considering Multi-Perspective Indices

As a significant technology in the automotive manufacturing industry, weight reduction in vehicle design has attracted much attention. Its effect on energy saving and emission reduction is prominent. The application of lightweight material is commonly adopted as a primary way of weight reduction. However, material selection is often subject to multi-perspective performance characteristics, e.g., mechanical and societal properties, and therefore, an effective multi-criteria decision-making (MCDM) method is needed. This paper presents a systematic hierarchical structure of multi-perspective indices for optimal lightweight material selection, including mechanical, durability, societal, and technical properties. A hybrid evaluation approach (G-TOPSIS) integrating grey relation analysis and technique for order performance by similarity to ideal solution (TOPSIS) is applied to evaluate lightweight material alternatives and obtain an optimal one. A case study, i.e., 17 kinds of lightweight materials, is conducted to verify the hierarchical structure and the MCDM method. In addition, a sensitivity analysis is conducted to monitor the robustness of solution ranking to changes. The results show that this method provides an effective decision-making tool for optimal lightweight material selection for automobile applications.

[1]  Anna Björklund,et al.  A material selection approach to evaluate material substitution for minimizing the life cycle environmental impact of vehicles , 2015 .

[2]  Jian-Xin You,et al.  A novel hybrid multiple criteria decision making model for material selection with target-based criteria , 2014 .

[3]  Mahmoud Abdelhamid,et al.  Using Quality Function Deployment and Analytical Hierarchy Process for material selection of Body-In-White , 2011 .

[4]  Guangdong Tian,et al.  Green material selection for sustainability: A hybrid MCDM approach , 2017, PloS one.

[5]  L B Lave,et al.  A life-cycle comparison of alternative automobile fuels. , 2000, Journal of the Air & Waste Management Association.

[6]  F. Findik,et al.  Materials selection for lighter wagon design with a weighted property index method , 2012 .

[7]  Serkan Yavuz,et al.  Weapon selection using the AHP and TOPSIS methods under fuzzy environment , 2009, Expert Syst. Appl..

[8]  G. Kushwaha,et al.  Green initiatives: a step towards sustainable development and firm's performance in the automobile industry , 2016 .

[9]  Basar Öztaysi,et al.  A decision model for information technology selection using AHP integrated TOPSIS-Grey: The case of content management systems , 2014, Knowl. Based Syst..

[10]  Zhiwu Li,et al.  Operation patterns analysis of automotive components remanufacturing industry development in China , 2017 .

[11]  S. Si,et al.  DEMATEL Technique: A Systematic Review of the State-of-the-Art Literature on Methodologies and Applications , 2018 .

[12]  MengChu Zhou,et al.  AHP, Gray Correlation, and TOPSIS Combined Approach to Green Performance Evaluation of Design Alternatives , 2018, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[13]  Kannan Govindan,et al.  Sustainable material selection for construction industry – A hybrid multi criteria decision making approach , 2016 .

[14]  Jian-Xin You,et al.  Evaluating health-care waste treatment technologies using a hybrid multi-criteria decision making model , 2015 .

[15]  Hu-Chen Liu,et al.  Material selection using an interval 2-tuple linguistic VIKOR method considering subjective and objective weights , 2013 .

[16]  Guangdong Tian,et al.  Technology innovation system and its integrated structure for automotive components remanufacturing industry development in China , 2014 .

[17]  Madjid Tavana,et al.  Multi-objective control chart design optimization using NSGA-III and MOPSO enhanced with DEA and TOPSIS , 2016, Expert Syst. Appl..

[18]  Mohammed A. Omar,et al.  Quantifiable measures of sustainability: A case study of materials selection for eco-lightweight auto-bodies , 2013 .

[19]  José T. San-José,et al.  A proposal for environmental indicators towards industrial building sustainable assessment , 2007 .

[20]  Basar Oztaysi,et al.  A decision model for information technology selection using AHP integrated TOPSIS-Grey , 2014 .

[21]  Guangdong Tian,et al.  Green decoration materials selection under interior environment characteristics: A grey-correlation based hybrid MCDM method , 2018 .

[22]  MengChu Zhou,et al.  Disassembly Sequence Planning Considering Fuzzy Component Quality and Varying Operational Cost , 2018, IEEE Transactions on Automation Science and Engineering.

[23]  Paul Joseph,et al.  Sustainable Non-Metallic Building Materials , 2010 .

[24]  Chris Manzie,et al.  Fuel economy improvements for urban driving : Hybrid vs. intelligent vehicles , 2007 .

[25]  Xiao-Bing Hu,et al.  Multi-objective optimization of material selection for sustainable products: Artificial neural networks and genetic algorithm approach , 2009 .

[26]  Prasenjit Chatterjee,et al.  Materials selection using complex proportional assessment and evaluation of mixed data methods , 2011 .

[27]  Nilanjan Dey,et al.  Case-Based Reasoning for Product Style Construction and Fuzzy Analytic Hierarchy Process Evaluation Modeling Using Consumers Linguistic Variables , 2017, IEEE Access.

[28]  Hu-Chen Liu,et al.  Health-Care Waste Treatment Technology Selection Using the Interval 2-Tuple Induced TOPSIS Method , 2016, International journal of environmental research and public health.

[29]  H. Jung,et al.  A lightweight design approach for an EMU carbody using a material selection method and size optimization , 2016 .

[30]  Jan-Anders E. Månson,et al.  Assessing the life cycle costs and environmental performance of lightweight materials in automobile applications , 2011 .