AI-Driven Electrical Machine Design: From Surrogate-Assisted Optimization to Trustworthy, Manufacturable, and Sustainable Design Workflows

Electrical machine design faces growing constraints from power density, wide operating ranges, thermal and mechanical limits, acoustics, manufacturability, cost, and critical material availability. While finite element and multi-physics simulations remain essential, their direct use in population-based or multi-objective optimization is often computationally prohibitive. AI, spanning surrogate modeling, machine learning, deep learning, physics-informed networks, Bayesian optimization, and emerging generative methods, is increasingly used to accelerate analysis, enlarge design spaces, and support inverse or multi-objective tasks. This review examines AI-assisted electrical machine design from a workflow perspective, distinguishing functional approximation, performance prediction, topology-aware learning, physics-informed modeling, active learning, and robust optimization under uncertainty. It highlights current limitations, including narrow topology coverage, reliance on FEM-generated data, weak extrapolation, limited uncertainty reporting, scarce experimental validation, and insufficient attention to manufacturability and sustainability. A design-readiness framework and minimum reporting checklist are proposed to improve trustworthiness and reusability. The review concludes that AI should serve as a physics-aware, validation-dependent accelerator, complementing, not replacing, electromagnetic expertise, multi-physics simulation, and prototype testing.