It is of academic value and significance to study the robust optimization design of spiral bevel gears with random installation errors. In the process of robust optimization design, there is a wide range of knowledge involved, and there is an intersection between probability theory, stochastic mathematics and other disciplines, which requires more time to study and explore.
(1) Robust design of spiral bevel gear under static state. In the case of installation error, the design parameter with low sensitivity to installation error can be obtained by taking the minimum sensitivity to installation error as the design objective. However, due to the randomness of the installation error, small deviation will cause large changes in the calculation results. In this case, certain assumptions are usually used to deal with it. For example, Huang T et al. assumed the installation error value in advance, carried out ETCA analysis, and obtained the transmission error curve and contact mark graph containing the installation error. Taking the minimum sensitivity of the installation error as the goal, BP neural network was used to optimize the second-order contact parameters, so that the sensitivity of the spiral bevel gear to the installation error was the lowest. However, when the analysis problem was relatively complex, the processing results often did not meet the expected requirements. How to improve the reliability of the design results, and combine the optimization method with the new algorithm to improve the efficiency of the solution, still need in-depth study.
(2) While considering the static robust design, we should also consider the dynamic robust design performance. In the actual working environment, the motion of spiral bevel gears is mostly dynamic meshing, and the reliability of spiral bevel gears will be affected by the interference of loads and other factors. However, due to the complexity of dynamic robust optimization design, the research on dynamic robust design of spiral bevel gears with the goal of minimum installation error sensitivity is less. In the later stage, the dynamic robust design of spiral bevel gears is the direction of in-depth research by many scholars.
(3) Through robust optimization design, the sensitivity of spiral bevel gear to installation error is reduced, and its coincidence degree will also be reduced accordingly. Therefore, in the robust optimization design of spiral bevel gears, the problem of not only reducing the installation error sensitivity, but also improving the coincidence degree needs further study.
(4) The fuzzy optimization design theory should be applied to the robust optimization design of spiral bevel gears considering the installation error.