Enhancing Steel Wheel Ventilation Efficiency Through Multi-Objective Optimization

This study focuses on the optimization of ventilation hole design in steel wheels used for heavy commercial vehicles. The primary objective is to reduce the weight of the wheel while ensuring compliance with radial fatigue and cornering fatigue test requirements. Four distinct ventilation types were parametrized using ANSYS Mechanical, with the von Mises stress on the disk, number of ventilations, and wheel weight serving as design parameters. Stress analysis and weight comparisons were performed between wheels featuring different ventilation types and an ellipse ventilation wheel. Incorporating the design of experiment (DoE) and response surface optimization (RSO) module in ANSYS Workbench 2022 R1 was employed to compare and evaluate the obtained values. Subsequently, the multi-objective genetic algorithm (MOGA-II) method was employed for optimization, aiming to identify the optimal design. The optimization process, utilizing a maximum of 20 iterations, a convergence stability percentage of 2%, and a maximum allowable Pareto percentage of 70%, yielded 1, 3, 3, and 3 candidate design points for round, slot, trapezoid, and halfmoon-type ventilation holes, respectively. Among the various ventilation types considered, the halfmoon-type ventilation hole exhibited the most promising results. Compared to the current design, the optimized wheel achieved a weight reduction of 0.9 kg (2.05%). This outcome demonstrates the effectiveness of the proposed methodology. Although lighter designs were not attainable while maintaining the same stress values for the other three ventilation types, the halfmoon-type ventilation hole was ultimately selected as the preferred design.