Abstract: Due to the difficulties of processing lenses with complex surface, slow tool servo is applied in the turning of lenses. An exponential model, based on the five main cutting parameters including tool nose radius, feed, depth of cut, spindle speed and discrimination angle, for surface roughness prediction of lenses is developed by means of orthogonal experiment regression analysis. Meanwhile, a prediction model of surface roughness based on least squares support vector machine (LS-SVM) with radial basis function (RBF) is constructed. And orthogonal experiment swatches are studied, crossed grid search and leave-one-out cross-validation (LOO-CV) is applied to determine the model parameters. The comparison of LS-SVM model and exponential model is also carried out. Predictive LS-SVM model is found to be capable of better predictions for surface roughness and has correlation coefficient R2 of 0.998 85, the root mean square error of 10.95%, and the mean absolute percent error of 9.28%. The experimental results and prediction of LS-SVM model show that effects of tool nose radius and feed are more significant than that of depth of cut on surface roughness of lenses turning.

Key words: Least squares support vector machine, Lense, Orthogonal regression analysis, Prediction model, Slow tool servo