TY - GEN
T1 - Tool Wear Monitoring Technology Based on Deep Learning of Machining State Features
AU - Li, Jiadong
AU - Zhang, Niansong
AU - Wang, Aimin
AU - Zhang, Zexian
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Aiming at the problem of real-time monitoring of the tool in the working state when the machine tool is producing the same part in batches, a tool wear state monitoring method based on the spindle power signal and the machining parameters of the machine tool is proposed. First, the linear model of deep learning is used to fit the linear model between machining parameters, tool wear and spindle power by using historical data, and then by setting the wear amount of the tool, the threshold value of the spindle power under different wear conditions is obtained, and the wear amount of the tool is monitored by judging the relationship between the spindle power and the threshold value. High availability compared to traditional downtime measurement methods. Finally, through the experiment, Collect the spindle power signal of the vertical machining centre to predict the wear state of the tool, and compare it with the actual wear amount. The results show that the proposed method has high accuracy and applicability, it can realize long-term online monitoring of tool wear under normal production conditions.
AB - Aiming at the problem of real-time monitoring of the tool in the working state when the machine tool is producing the same part in batches, a tool wear state monitoring method based on the spindle power signal and the machining parameters of the machine tool is proposed. First, the linear model of deep learning is used to fit the linear model between machining parameters, tool wear and spindle power by using historical data, and then by setting the wear amount of the tool, the threshold value of the spindle power under different wear conditions is obtained, and the wear amount of the tool is monitored by judging the relationship between the spindle power and the threshold value. High availability compared to traditional downtime measurement methods. Finally, through the experiment, Collect the spindle power signal of the vertical machining centre to predict the wear state of the tool, and compare it with the actual wear amount. The results show that the proposed method has high accuracy and applicability, it can realize long-term online monitoring of tool wear under normal production conditions.
KW - Linear model
KW - Tool wear
KW - real-time measurement
UR - https://www.scopus.com/pages/publications/85137807938
U2 - 10.1109/ICMA54519.2022.9856213
DO - 10.1109/ICMA54519.2022.9856213
M3 - Conference contribution
AN - SCOPUS:85137807938
T3 - 2022 IEEE International Conference on Mechatronics and Automation, ICMA 2022
SP - 146
EP - 151
BT - 2022 IEEE International Conference on Mechatronics and Automation, ICMA 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 19th IEEE International Conference on Mechatronics and Automation, ICMA 2022
Y2 - 7 August 2022 through 10 August 2022
ER -