Multi-Objective Optimization Design of Assembled Wheel Lightweight Based on Implicit Parametric Method and Modified NSGA-II

• Published in: IEEE access Vol. 11; pp. 71387 - 71406
• Main Authors: Zhang, Shuai, Li, Ruixu, Lu, Dongzhen, Xu, Liyou, Xu, Wenchao
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Aluminum base alloys; Assembled wheel; Bending; bending and radial fatigue performance; Bending fatigue; Design optimization; entropy-weighted grey relation analysis; Fatigue; Fatigue life; Fatigue tests; Finite element method; Genetic algorithms; Lightweight; lightweight multi-objective optimization; Magnesium base alloys; Multiple objective analysis; Optimization; Parameterization; Parametric statistics; Pareto optimization; shell-body element combination; Sorting; Sorting algorithms; Steel; Weight reduction; Wheels
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2023.3279277

To improve the fatigue life of the wheel, the level of lightweight design, and the efficiency and accuracy of optimization analysis. In this paper, a wheel finite element analysis model combining shell-body elements is established, and a design and optimization method for an assembled wheel with a magnesium alloy rim-aluminum alloy spoke structure is proposed. Based on the advanced grid deformation technology and implicit parameterization technology, the assembled wheel fatigue analysis parametric model is created, DOE sampling is carried out, and the design variables are screened out by combining the contribution analysis method. A hybrid method of entropy weighted grey relation analysis (EGRA) combined with modified non-dominated sorting genetic algorithm-II (MNSGA-II) is proposed for multi-objective optimization of the assembled wheel in combination with an approximate model approach to obtain the Pareto frontier solution set and its grey relation order to filter the preferred compromise solution. The simulation analysis compares and optimizes various performance indicators of the front and rear assembled wheels, and is verified by bending and radial fatigue tests. The results show that the weight of the wheel is reduced by 10.17%, and the weight reduction effect is remarkable. Under the condition of ensuring the calculation accuracy, the wheel shell-volume element combination model proposed in this paper saves at least 46.44% of the calculation time compared with the volume element model.