Collaborative Optimization on Both Weight and Fatigue Life of Fifth Wheel Based on Hybrid Random Forest with Improved BP Algorithm - Huan Xue; Chang Guo; Xiaojian Peng; Saiqing Xu; Kaixian Li; Jianwen Li (Applied Sciences)
The fifth wheel of the semi-trailer tractor is a key component connecting the tractor and the semi-trailer. During operation, the fifth wheel experiences frequent irregular and repetitive loading conditions. This leads to a decline in its durability and fatigue life, which can significantly impact the efficiency of cargo transport. The lightweight design enhances both the transport efficiency and fuel economy of the semi-trailer tractor. In this research, to achieve weight reduction while maintaining the wear-resistant failure protection performance in semi-trailer tractors, we selected a new material—special steel for saddles (SD600). Its stress-strain and fatigue life were analyzed under static compression, uphill lifting, and steering rollover conditions. These findings confirm the necessity of implementing lightweighting measures. Using a multi-objective genetic algorithm, we established an optimization model aimed at balancing weight reduction and fatigue life enhancement. As a result, the optimized fifth wheel achieved a 24.11% reduction in mass, while its fatigue life increased by 15 times, thus realizing the synergistic optimization of weight and fatigue life. We proposed a prediction model combining a random forest algorithm with an optimized back propagation (BP) neural network. Compared to the traditional BP approach, this model improved the mean absolute percentage error (MAPE) by 47.62%. Quadratic optimization was conducted based on the optimal design option set, using data analysis to determine the range of values of each variable under specific constraints and to verify the stress-strain and fatigue life for very small values in the range.