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

Creative Commons License

Noğay H. S.

8th International Bursa Scientific Research Congress December 16-18, 2025, Bursa, TURKEY, 16 - 18 Aralık 2025, ss.842-851, (Tam Metin Bildiri)

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Sayfa Sayıları: ss.842-851
  • Açık Arşiv Koleksiyonu: AVESİS Açık Erişim Koleksiyonu
  • Bursa Uludağ Üniversitesi Adresli: Evet

Özet

ABSTRACT 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 (R² = 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. Keywords: Support Vector Machines, Slot Geometry, Coil Height Prediction, Rotary Electric Machines, Machine Learning