Surface Quality Prediction of Atmospheric Pressure Plasma Arc Cleaning Based on Least Square Support Vector Machine

Abstract

The theory of Least Squares Support Vector Machines was applied to metal surfaces cleaning by atmospheric pressure plasma arc. An intelligent predictive model of the non-linear relationship between cleaning quality and process parameters was established with the k-fold cross training of sample data. An orthogonal experiment was conducted to assess the effect of processing parameters on surface quality. The experimental results and predicted values show that the atmospheric pressure plasma arc (APPA) cleaning is effective in reducing considerably the amount of lubricant. Furthermore, it is feasible to apply LS-SVM in forecasting the cleaning quality and determining processing parameters, and the mean absolute percent error eMAPE between predictive value and experimental value of water contact angle is 6.09%. Otherwise, the eMAPE of working current is 4.46%.