Experimental and statistical analysis of the effects of punching and laser cutting methods on induction motor efficiency and total magnetic losses in silicon lamination sheets


Bayraktar Ş., Turgut Y.

JOURNAL OF MAGNETISM AND MAGNETIC MATERIALS, vol.572, pp.1-10, 2023 (SCI-Expanded)

Abstract

In this study, M400-50A electrical steel laminations (ESLs) used in electrical machines were cut with punching and laser methods and the effect of these cutting methods on the efficiency and total losses of induction motors was investigated. ESLs were prepared by laser and punching cutting methods concerning an induction motor with a power of 5.5 kW. After the stator and rotor packages were created, processes such as winding, varnishing, and mechanical assembly were performed on the motors. It is prepared for motor efficiency (MEfficiency) and total magnetic losses (TLosses) tests according to the direct measurement method specified in the IEC 60034-2-1-1A standard. Four different frequencies (f: 50, 75, 100 and 125 Hz) and five different motor loading rates (MLoad: 25, 50, 75, 100 and 125%) were defined as independent variables in the tests. MEfficiency and TLosses values were measured as dependent variables. Plastic deformation and edge rounding were observed on the edges cut with punching and edge rounding and angled edge formation on the edges cut with the laser. The average surface roughness of the laser-cut edges was determined to be higher than the ESLs cut by punching. It was observed that TLosses increased while MEfficiency decreased with increasing frequency and MLoad value in motor tests. It was determined that the training, testing, and experimental data are quite compatible with each other and the R2 values are close to 1 in the statistical analyzes performed with ANN (Artificial neural network). In addition, it was specified that RMSE (Root mean square error) and MAPE (Mean absolute percentage error) values vary between 0.007 and 13.310% for each cutting method and mathematical models can be used to predict MEfficiency and TLosses with ANN.