Gear misalignment optimization for Electric Drive Unit using Machine learning based Prescriptive Analytics

Gear mesh misalignment in electric drive unit may result in shifts in the load distribution of a gear pair that can increase contact and bending stresses. It may move the peak bending and contact stresses to the edge of the face width, and also increase gear noise. Lower misalignment value is often required to reduce the peak bending and contact stresses and have a balanced load distribution along the gear flank. This paper delineates a method which is also called prescriptive analytics that combines virtual simulations, Machine learning (ML) and optimization techniques to minimize different gear misalignments for electric vehicle drive unit. The gear misalignments are dependent on stiffness of different components in the drive unit. So, a comprehensive approach that incorporates effect of individual components and their interactions is needed. Generally, an assembly level virtual simulation is performed to evaluate gear misalignments. In order to minimize the misalignments, manual optimization process is carried out by sequential modifications of stiffness of individual components. However, this process is laborious and does not account for interactions between components. To mitigate this challenge, prescriptive analytics methodology is used to achieve the optimal combinations of stiffnesses of all components. In this study, Machine learning models are developed based on a sample of design of experiments (DOE) simulations. These ML models are used as surrogates for actual simulations in generic algorithms (Differential evaluation) based optimization techniques. It prescribes changes in stiffness of different components to get optimum misalignment value.