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

IEEE Access

Yongfeng Pu¹  Junyuan Zhang¹  Meng Yang¹  Fangwu Ma¹ 

[1] State key Laboratory of Automotive Simulation and Control, Jilin University, Changchun, China;

关键词: Material selection;    automobile applications;    decision making;    data modeling;    Internet-of-Things;   

DOI  :  10.1109/ACCESS.2018.2804904

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown