For the purpose of selecting appropriate cutting parameters to meet desired surface quality requirement, a surface roughness prediction model based on least square support vector machine (LSSVM) and particle swarm optimization (PSO) was proposed. The prediction accuracy and convergence speed were used as indicators, the PSO-LSSVM model was compared with support vector machine, artificial neural network and genetic algorithm based back-propagation neural network model. The results show that PSO-LSSVM model has a relatively higher prediction accuracy and faster convergence speed. A surface roughness prediction and parameter optimization application system was developed based on MATLAB GUI. The application system has good practicability, so simple and fast prediction of surface roughness can be realized and it helps decision makers to select cutting parameters flexibly.
YANG Zhao, LU Chaofan, LIU Anli. Surface Roughness Prediction Model and Application Based on PSO-LSSVM Algorithm[J]. Machine Tool & Hydraulics,2021,49(6):47-50