Artificial intelligence for springback compensation with electric vehicle motor component

Abstract

Hairpin, which is used for a drive motor of electric vehicle, is formed from copper coil coated with very thin enamel layer to its desired shape by successive forming processes. The unavoidable springback of hairpin forming has induced critical problems in fabricating the drive motor of electric vehicle that is directly related to the product performance of the motor. In this research, a new method for the springback compensation for hairpin forming was proposed by using artificial intelligence (AI). The AI model calculates the eight springback parameters with respect to the given inputs: material properties, cross-sectional dimension, and die spotting. The training dataset for AI model is based on finite element simulation. Material properties of copper and thin enamel layer were characterized by combined methods: newly designed specimen, nanoindentation, and inverse analysis. For springback compensation, the die spotting value representing the gap between die and punch (or punch stroke) was optimized by using simplex optimization algorithm with the trained AI model. The results showed that the springback could be effectively compensated with the proposed method in a few seconds.