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.
Shi et al. (Fri,) studied this question.