Research on Multi-Objective Optimization of Helical Gear Shaping Based on an Improved Genetic Algorithm

Traditional design and shaping methods of helical gears may have difficulties in meeting the requirements of multiple performance indicators simultaneously, such as tooth surface accuracy, load-carrying capacity, and transmission efficiency. This study attempts to overcome these limitations through a multi-objective optimization method and achieve the comprehensive optimization of multiple performance indicators. This paper aims to boost gear system power transmission and cut vibration and noise. It assesses gear shaping impacts via normal load per unit length of the helical gear surface and gear vibration amplitude. Traditional gear shaping schemes were first determined using classic theories and formulas. Then, an improved genetic algorithm was applied to seek optimal helical gear shaping parameters. An eight-degree-of-freedom lumped mass model of the helical gear transmission system, considering bending–torsion–axial coupling, was developed based on Newton’s second law and solved via the fourth-order Runge–Kutta method. Comparisons showed that the traditional shaping scheme reduced the maximum normal load per unit length by 20.6% and the system’s vibration amplitude by 18.3%. In contrast, the improved genetic algorithm achieved greater reductions of 26.34% and 27.2%, respectively. Both methods effectively decreased the maximum normal load per unit length and system vibration amplitude, with the improved genetic algorithm yielding superior results. This work offers a key theoretical basis and reference for enhancing load transmission, reducing costs, and mitigating vibration and noise in gear transmission systems.