The purpose of this study is improvement of metal identification performance with step response. Feature values are maximum derivative current and its reaching time, these values depend on lift-off in the range of 0.5-1.5 mm. As a result of metal identifications, decision tree is the fastest and highest accuracy in 4 machine learning models. When increasing training samples, calculation time of all models are increasing, and accuracies are saturated 100 samples. When comparing between data whose lift-off is from 0.5 to 1.5 mm and data that fixed lift-off, classification accuracy in data fixed lift-off is improve than one in data not fixed lift-off.
J-STAGEがリニューアルされました！ https://www.jstage.jst.go.jp/browse/-char/ja/
