Integrated multi-objective optimization on dual-modified spur gear flanks with enhanced contact and lubrication performance

Spur gears are widely integrated into gearing systems due to their manufacturing and power transmission efficiency. Nevertheless, their dynamic response characteristics, specifically noise, vibration, and wear, demonstrated significant limitations within high-speed operation. Therefore, this study aims to integrate multi-objective optimization into the dual modification of spur-gear flanks to minimize the peak-to-peak transmission error (PPTE) and maximize the minimum lubricant film thickness. First, the uniform design method is used to define the optimization variable sample point table. The PPTE and minimum lubricant film thickness are calculated using KISSsoft software to generate the training data. An approximation model of the objective factors is obtained using an RBF neural network. Finally, the optimal result is predicted by applying the PSO algorithm. The simulation results show the optimized dual modification reduced PPTE by 28.81 %, 8.7 %, 35.71 %, 35.71 %, 47.62 %, and 47.62 % and increased minimum film thickness by 1.25 %, 3.0 %, 11.57 %, 14.61 %, 17.98 %, and 23.03 % compared to the standard case, the un-optimized dual modification case, the short and long lead/double-crowning modification cases, respectively. It verifies the effectiveness of the optimization process.