Speedup of Flux Waveforms Control Using Deep Neural Network for Single Sheet Tester

To achieve high efficiency of various motors, the realization of the high saturation magnetic flux density and thinning of the electrical steel sheets are required. When the performances of various motors are verified using the finite element method (FEM), the accurate magnetic property of the electrical steel sheet is applied to FEM. However, because extended elapsed time is taken to measure the magnetic property in the high flux region when the conventional feedback control is applied to the flux waveform control, the method for reducing the measurement time is essential. In this paper, a waveform control method that evaluates the initial waveform using deep neural networks (DNN) is proposed. The performance of the proposed system supported by the DNN is verified and compared to that supported by only the conventional feedback formula.