Akademik Veri Yönetim Sistemi
4th INTERNATIONAL CONGRESS ON MATERIALS ENGINEERING AND ADVANCED MANUFACTURING TECHNOLOGIES CONGRESS, İstanbul, Türkiye, 26 Ocak 2025, ss.101-110, (Tam Metin Bildiri)
This study investigates the effect of slot shapes on leakage flux in rotating electrical machines using a Decision Tree (DT) regression model. Slot shapes significantly influence the performance and efficiency of electrical machines by affecting the slot leakage flux and associated energy losses. To address this, a dataset of 755 entries representing various slot dimensions and their equivalent permeability was analyzed. The DT model demonstrated high accuracy, with an R² value of 0.99 during testing and a low Root Mean Squared Error (RMSE) of 0.0958. The results highlighted the model's ability to classify and predict the permeability of six distinct slot shapes, providing valuable insights for optimizing motor design. Graphical analyses further illustrated the variations in flux distribution across different slot types, emphasizing the advantages of machine learning in enhancing traditional motor design processes. This research contributes to the development of efficient, data-driven solutions for designing advanced electrical machines.