Self-Optimization In Gear Manufacturing And Assembly For Automotive Electric Drive Production

Abstract

Due to the trend of electrification in the automotive industry, the economic production of electric drives with high acoustic quality requirements is a crucial factor to stay competitive in the global market. Low noise levels in the interior are an important criterion for the perceived quality of electric vehicles. Consequently, the noise generated by mounted gear components within integrated electric drive topologies must be minimized. Gears with unavoidable manufacturing deviations are usually randomly assembled, leading to random non-defined gear-related acoustic properties of the assembled electric drive. Furthermore, parameters of the gear manufacturing machines do not dynamically adapt to unknown changes in the production system leading to non-ideal quality output. To address these challenges, this paper presents a self-optimization concept in gear manufacturing and assembly in the production of electric drives by cognition enhanced control. A digital twin is developed which estimates the transmission error based on in-line measurements. Through optimization, an optimal selection of gear pairs is achieved. Based on quality predictions, adaptive control of the gear manufacturing process can be implemented, leading towards a closed-loop self-optimization of the production system. The concept is developed and validated using an exemplary use case from the commercial vehicle industry.