Machine Learning in Transmission Design

Abstract In the design process of gear systems, various computational tools are used to optimize the operational behavior. Due to time constraints, the initial stages of development typically assume ideal, deviation-free gear geometries. However, deviations resulting from manufacturing significantly affect performance in later applications. This publication presents a concept to incorporate the influence of manufacturing tolerances during early development phases systematically. Based on an initial set of detailed simulations, a surrogate model is developed and trained. This model can quickly generate a wide range of results, efficiently accounting for the large variance of deviations. The shown approach combines the advantages of detailed simulations with the speed of machine learning models.