Adaptive data-driven collaborative optimization of both geometric and loaded contact mechanical performances of non-orthogonal duplex helical face-milling spiral bevel and hypoid gears. (December 2020) - Chen, Xuelin; Ding, Han; Shao, Wen
Highlights: Duplex helical tooth flank face-milling for the new non-orthogonal spiral bevel and hypoid gears. An improved tooth contact analysis (TCA) for the sensitivity assembly problem. NLTCA is integrated with the assembly error modification for establishing data-driven function relation. Multi-objective optimization (MOO) about loaded contact mechanical performance evaluations. Decision-making strategy for the adaptive data-driven collaborative optimization. Abstract: Considering the duplex helical face-milling characteristics, an innovative adaptive data-driven collaborative optimization of both tooth flank geometric accuracy and loaded contact mechanical performance evaluations is developed for non-orthogonal spiral bevel and hypoid gears. Firstly, an advanced duplex helical face-milling is simulated for tooth flank modeling and an improved tooth contact analysis (TCA) is proposed for the sensitive assembly problem. Numerical loaded tooth contact analysis (NLTCA) is used to determine data-driven relations between the loaded contact mechanical performance evaluations and assembly error. Then, a new adaptive data-driven collaborative optimization model is established by modifying assembly error evaluations. In addition to tooth flank geometric accuracy, the loaded contact mechanical evaluations including loaded contact pattern, loaded transmission error, loaded contact pressure and loaded contact stress are used as main targets. Here, to get high accuracy and efficiency, the decision-making of collaborative optimization is divided into two sub-systems: i) Loaded contact mechanical performance multi-objective optimization (MOO) for target flank determination. Here, an achievement function approach is used to get Pareto optimal solution. ii) Tooth flank geometry optimization by assembly error modification. Where, sensitivity analysis strategy is applied to select the optimal design variables. The given numerical instance can verify the proposed method.