An Effective PSO-LSSVM-Based Approach for Surface Roughness Prediction in High-Speed Precision Milling

Surface roughness is one of the important indicators to measure the surface quality of parts processed, in addition to the cutting parameters affecting the surface roughness, the inevitable tool wear during the cutting process also makes the surface roughness constantly changing. In order to achieve high-precision prediction of machined surface roughness, a high-speed precision milling surface roughness prediction method based on particle swarm optimization least squares support vector machine (PSO-LSSVM) is proposed in this paper. The prediction method uses standardized cutting parameters and tool wear as the input variables, and uses the LSSVM algorithm to model the relationship between the input variables and the surface roughness, the improved PSO algorithm is used to optimize the hyperparameters of LSSVM so as to improve the generalization ability. In order to verify the effectiveness and superiority of PSO-LSSVM predictive performance, two surface roughness prediction models have been developed based on support vector machine (SVM) and response surface method (RSM), respectively. With the same sample conditions, average relative error and root mean square error of PSO-LSSVM prediction model are the smallest, and the correlation coefficient of PSO-LSSVM method is the largest. It is indicate that the surface roughness prediction accuracy and generalization ability based on PSO-LSSVM are the best under different cutting parameters.