Machine Learning-Driven Multi-Objective Sizing Optimization, Performance Prediction and Feature Correlation Analysis of Vehicle Frame

Xianren Zhou, Zhongmin Wang, Guangshuai Xu, Yi Zheng, Deguang Li, Jun Lan, Feiyong Long, Longjie Li, Dianhui Wang, Huarong Liu, Zebing Xu, Chenggang Hao, Yonghua Shi

To overcome the challenges in conventional frame structure design, namely the difficulty in balancing lightweight design and performance enhancement, the low efficiency of finite element (FE) simulation, and the tedious process of multivariable preliminary screening, an efficient optimization framework for frame structures that integrates multi-objective size optimization, machine learning-based performance prediction, and feature correlation analysis is proposed. First, for the steel–aluminum hybrid frame (with the main load-bearing components made of 6005A aluminum alloy and the critical load-bearing supports and joints made of Q345 low-alloy high-strength steel), a trade-off solution is obtained through multi-objective size optimization. Verified by FE simulation, this solution reduces the frame mass by 6.37% and increases the torsional stiffness by 10.47% compared with the initial design. At the same time, the modal performance, structural strength, and deformation control capability are all significantly improved, achieving a precise balance between lightweighting and stiffness enhancement. Second, a linear regression prediction model is constructed to achieve high-accuracy predictions. The average prediction error for torsional stiffness is only 2%, and the maximum prediction error for the seventh-order modal frequency is less than 1%. The prediction time for a single sample is less than one second, which is more than 1000 times faster than conventional FE simulation, thus efficiently replacing time-consuming simulation analyses. Finally, feature correlation analysis is adopted as an alternative to traditional sensitivity analysis. The core variables identified by this method are highly consistent with those obtained from Hypermesh sensitivity analysis, enabling rapid multivariable screening without additional simulations and greatly improving the efficiency of the preliminary analysis phase. The proposed optimization framework achieves a favorable combination of optimization effectiveness, prediction accuracy, and design efficiency. It not only provides a feasible engineering solution for the lightweight design of frame structures but also serves as a technical reference for the efficient optimization of similar complex structures, demonstrating significant engineering application value.