Gene

Multiple-criteria decision analysis

Engineering

Aerospace engineering

Materials science

TOPSIS

Automotive industry

Robustness (evolution)

Material selection

Ideal solution

Selection (genetic algorithm)

Biochemistry

Data mining

Computer science

Mathematical optimization

Relation (database)

Thermodynamics

Physics

Ranking (information retrieval)

Operations research

Mathematics

Artificial intelligence

Composite material

Chemistry

Perspective (graphical)

Machine learning

IEEE Access

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.

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