Prediction of Cutting Temperature in the Milling of Wood-plastic Composite Using Artificial Neural Network

In the milling of wood-plastic composites, the cutting temperature has a great influence on tool life and cutting quality. The effects of cutting parameters on the cutting temperatures in the cutting zone were analyzed using infrared temperature measurement technology. The results indicated that the cutting temperature increased with the increase of spindle speed and cutting depth but decreased with the increase of feed rates. In addition, based on experimental data, a BP neural network model was proposed for predicting the cutting temperatures. The value of R-2 was 0.97354 for the testing data, which indicates that the developed model achieved high prediction accuracy, respectively. The results of the study can play a guiding role in the prediction and control of cutting temperature, which is of great importance in the improvement of tool life, machining quality, and machining efficiency.

Automated summary

The study analyzed the influence of cutting parameters on cutting temperatures in wood-plastic composites milling, and proposed a BP neural network model for predicting cutting temperatures with high accuracy.