DETERMINATION OF MAXIMUM COIL HEIGHT IN VARIOUS SLOT GEOMETRIES OF ROTARY ELECTRIC MACHINES USING SUPPORT VECTOR REGRESSION (SVR)

This study presents a Support Vector Regression (SVR)-based framework for predicting the maximum coil height in various slot geometries of rotary electric machines.Five practical slot forms were modeled using key geometric parameters, and a dataset was generated to reflect realistic dimensional ranges encountered in industrial machine design.The proposed SVR model, trained using MATLAB with 20-fold cross-validation, achieved excellent predictive performance (R = 0.9998, RMSE = 0.021), demonstrating near-perfect agreement between predicted and true values.Residual trends, parity plots, and Shapley-value-based feature importance analysis confirmed the accuracy and physical consistency of the model, with slot-depth parameters (h3, h4) identified as the most influential predictors.The results show that the proposed approach provides a fast, reliable, and computationally efficient alternative to repeated numerical simulations, enabling rapid evaluation and optimization of different slot forms.