Automatic data-driven operation and optimization of uncertain misalignment by considering mechanical power transmission performances of spiral bevel and hypoid gears

Abstract

The misalignment has always become an increasingly important factor affecting the mechanical power transmission performances for the spiral bevel and hypoid gears. Its uncertainty problem usually exists in the actual manufacturing and transmission. This paper presents an automatic data-driven operation and optimization to determine the uncertain misalignment. Firstly, an improved tooth contact analysis (TCA) model is given to identify the tooth contact point by using a novel kinematic arrangement. This improved TCA is used to establish the data-driven functional relationships of uncertain misalignment with respect to the mechanical power transmission performance evaluations, namely the transmission error, tooth stiffness and contact pressure distribution. Then, data-driven operation of the uncertainty problem of misalignment is transformed into a deterministic optimization by introducing P-model. Finally, the optimal interval of the uncertain misalignment is determined by using an automatic data-driven operation and optimization in consideration of the different uncertainty levels. Where, the nonlinear interval number optimization is employed to perform a multi-objective optimization of the performance evaluations. The given instance can assess the validity of the proposed methodology.