Development of a Surface Roughness Prediction Model for Slow Tool Servo Turning Machining

Abstract

To investigate the effect of slow tool servo turning process parameters on surface roughness, we established a high precision surface roughness prediction model. A guide to the selection of turning process parameters was compiled, and a turning test was conducted based on a response surface method (RSM) central composite design. ANOVA explores the influence law of process parameters on surface roughness. A RSM BP neural network model, and MEA-BP surface roughness model were established and the prediction performance of the three models was evaluated. The results show that the significant process parameters affecting surface roughness are tool radius, discrete angle, feed rate, and cutting depth in descending order; and the prediction errors of RSM, BP, and MEA-BP are 11.41%, 19.67%, and 5.54%. This suggests that the MEA-BP model has the highest prediction accuracy with the same test data, RSM is second, whilst the single BP model struggles to capture multiple data characteristics and its prediction accuracy is poor. In addition, MEA can effectively solve the BP model falling into local optimum and improve the model prediction accuracy. Process parameters Surface roughnessResponse surface method BP neural network Mind evolutionary algorithm