Enhanced Multi‐Objective Design Optimisation of Salient Pole Reluctance Magnetic Gear Using Bayesian‐Optimised Artificial Neural Networks

ABSTRACT The application of artificial intelligence in magnetic gear design has opened new avenues for accelerating computation and optimisation processes. In this paper, a Bayesian‐optimised artificial neural network (ANN) was presented as a surrogate model to predict the performance of salient pole reluctance magnetic gears (SP‐RMGs). The model focuses on key performance indicators such as average torque, torque ripple, and total weight. A diverse dataset generated through Latin hypercube sampling (LHS) is used to train the ANN, which employs customised activation functions to accurately capture the non‐linear characteristics of the magnetic gear. Bayesian optimisation is applied to fine‐tune the hyperparameters, resulting in a significant reduction in computational time. The proposed approach leverages deep learning to efficiently accelerate the multi‐objective optimisation process, providing accurate predictions of SP‐RMG performance metrics. The optimisation results demonstrate significant improvements with the model predicting optimal design parameters that enhance torque performance, reduce torque ripple by 47.2%, and decrease total weight. The proposed approach offers a substantial reduction in computational time while delivering precise optimisation outcomes.