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Journal of Automotive Safety and Energy ›› 2025, Vol. 16 ›› Issue (4): 620-628.DOI: 10.3969/j.issn.1674-8484.2025.04.012

• Intelligent Driving and Intelligent Transportation • Previous Articles     Next Articles

Multi-objective structural optimization of heavy truck frame based on SIMP algorithm and GRSM algorithm

ZHANG Xiao1,2(), LIU Yong1, JIANG Xuesheng3, LIAO Yilong4, HE Feng1,*()   

  1. 1 School of Mechanical Engineering, Guizhou University, Guiyang 550025, China
    2 BYD Company Limited, Shenzhen 518119, China
    3 Guizhou Changjiang Automobile Co., Ltd., Guiyang 550025, China
    4 Guiyang University of Information Technology, Guiyang 550025, China
  • Received:2024-11-15 Revised:2025-03-25 Online:2025-08-30 Published:2025-08-27

Abstract:

A multi-objective structural optimization on high-strength lightweight design was conducted to address the issues of the deformation and cracking in heavy truck frames. A finite element model of the frame was established by using a pre-processing software Hypermesh with taking a specific 11-meter heavy truck frame as the research subjects. The original heavy truck frame underwent multi-condition multi-objective topology optimization using the Solid Isotropic Material with Penalization (SIMP) method to identify the optimal load-bearing structure. The global response surface method (GRSM) was employed for multi-objective dimensional optimization of the heavy truck frame to reduce its mass. The static and the modal analyses were performed on the frame under the conditions of the full-load bending, the full-load torsion, the full-load cornering, and the full-load braking. The results show that the optimized frame achieves an average stiffness increase of at least 21.1%, a minimum increase of 8.9% in low-order average dynamic frequency, and a mass reduction of 3.2%. Therefore, this has enabled the high-strength lightweighting of heavy truck frames.

Key words: heavy truck, frame construction, solid isotropic material with penalization (SIMP), global response surface method (GRSM), multi-objective size optimization

CLC Number: