S&M2854 Research Paper of Special Issue
Published: February 28, 2022
Using Neural Networks for Tool Wear Prediction in Computer Numerical Control End Milling [PDF]
Cheng-Hung Chen, Shiou-Yun Jeng, and Cheng-Jian Lin
(Received September 6, 2021; Accepted January 4, 2022)
Keywords: backpropagation neural network, tool wear prediction, linear regression, machine tool, milling
The precision of the machining tool in computer numerical control (CNC) machining is affected by several factors. For example, cutting parameters considerably affect machining accuracy and tool wear. Tool wear results in the manufacture of substandard products. Therefore, predicting tool wear is crucial in CNC machining. In this study, we proposed a backpropagation neural network (BPNN) to predict tool wear. In machine learning, backpropagation is a widely used algorithm for training artificial neural networks. The proposed BPNN considered the variation of tool wear with different cutting parameters, such as the spindle speed, feed, cutting depth, and cutting time. The experimental results revealed that the root mean square error of the BPNN prediction model was less than that of the linear regression prediction model. Furthermore, the proposed model achieved a coefficient of determination (R2) of 0.9964, which indicated that the BPNN model can accurately predict tool wear.Corresponding author: Cheng-Jian Lin
This work is licensed under a Creative Commons Attribution 4.0 International License.
Cite this article
Cheng-Hung Chen, Shiou-Yun Jeng, and Cheng-Jian Lin, Using Neural Networks for Tool Wear Prediction in Computer Numerical Control End Milling, Sens. Mater., Vol. 34, No. 2, 2022, p. 803-817.