Abstract
Specific total loss is one of the most important evaluation indexes for the magnetic properties of non-oriented electrical steel sheets. The aim of this study is to investigate the influencing mechanisms of laser cutting parameters as well as the sample characteristics on the specific total loss of thin non-oriented electrical steel sheets processed by laser cutting using a machine learning method. Eight input parameters were finally considered; namely, silicon and manganese contents, thickness of the steel sheets, laser nozzle diameter, laser power, cutting speed, the pressure of process gas, and laser defocus, while one output parameter, the specific total loss, was evaluated. It was found that the specific total loss was positively correlated with the sample thickness, but negatively correlated with silicon and manganese contents, the process gas pressure and laser nozzle diameter. In addition, laser power and cutting speed exhibit complicated non-linear relationships with the specific total loss.
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