Prediction and Analysis of the Surface Roughness in CNC End Milling Using Neural Networks

In the metal cutting process of machine tools, the quality of the surface roughness of the product is very important to improve the friction performance, corrosion resistance, and aesthetics of the product. Therefore, low surface roughness is ideal for mechanical cutting. If the surface roughness of the product can be predicted, not only the quality of the product can be improved but also the processing cost can be reduced. In this study a back propagation neural network (BPNN) was proposed to predict the surface roughness of the processed workpiece. ANOVA was used to analyze the influence of milling parameters, such as spindle speed, feed rate, cutting depth, and milling distance. The experimental results show that the root mean square error (RMSE) obtained by using the back propagation neural network is 0.008, which is much smaller than the 0.021 obtained by the traditional linear regression method.

Automated summary

In the metal cutting process of machine tools, the quality of the surface roughness is crucial for improving product performance. This study proposes a back propagation neural network (BPNN) to predict surface roughness and analyzes the influence of milling parameters using ANOVA. Experimental results show that the BPNN method achieves higher prediction accuracy.