Automated design and optimization of electric motors via an intelligent CAD/CAE integration platform

Abstract

Traditional electric motor design optimization is often slowed by fragmented CAD/CAE tools, repetitive manual modeling, and heavy user intervention, resulting in long design cycles and low productivity. To address this challenge, this paper proposes an intelligent integrated CAD/CAE platform that unifies automated 3D parametric modeling, electromagnetic finite-element simulation, and multi-objective optimization in a closed workflow. Specifically, NX APIs in C++ generate batches of parametric motor models from user-defined design variables, while Python scripts based on PyAEDT import the NX-exported models into ANSYS Electronics Desktop (AEDT) and execute end-to-end simulations without manual intervention. A Gray Wolf Optimizer (GWO) is further used to tune a Kriging surrogate model for multi-objective optimization, enabling efficient design decision-making based on simulation outputs. Experimental results show that the proposed platform reduces repetitive modeling and shortens user operation time by over 88% compared with conventional practice, and the optimized motor achieves a 29% increase in output torque. Overall, the platform provides an effective route to accelerated motor development and demonstrates strong potential for engineering applications and industrial deployment.