On-line Monitoring of Tool Wear Conditions in Machining Processes Based on Machine Tool Data

被引：3
|
作者：
Lu Z. [1 ]
Ma P. [1 ]
Xiao J. [2 ]
Wang M. [1 ]
Tang X. [1 ]
机构：
[1] School of Mechanical Engineering and Automation, Beihang University, Beijing
[2] CRRC Zhuzhou Institute Co., Ltd., Zhuzhou, 412005, Hunan
来源：
Zhongguo Jixie Gongcheng/China Mechanical Engineering | 2019年 / 30卷 / 02期
关键词：
Convolutional neural network; Object linking and embedding for process control unified architecture(OPC UA); On-line monitoring; Tool wear;
D O I：
10.3969/j.issn.1004-132X.2019.02.013
中图分类号：
学科分类号：
摘要：
To realize the on-line monitoring of tool wear conditions, and improve the feasibility of monitoring system in machining processes, an on-lime cutting tool condition monitoring method was proposed based on machine tool data in machining processes. OPC UA was used for NC machine tool data acquisition and storing, and the internal machine process informations related to tool wear conditions were collected. Based on the process informations and corresponding wear informations, convolutional neural network was used to establish a recognition model of tool wear conditions. The performance of proposed method was verified in machining cases, and compared with other tool wear condition monitoring methods. This method is more suitable for tool wear condition monitoring in practical machining processes. © 2019, China Mechanical Engineering Magazine Office. All right reserved.
引用
收藏
页码：220 / 225
页数：5
相关论文
共 14 条
  • [1] Chungchoo C., Saini D., On-line Tool Wear Estimation in CNC Turning Operations Using Fuzzy Neural Network Model, International Journal of Machine Tools & Manufacture, 42, 1, pp. 29-40, (2002)
  • [2] D'addona D.M., Ullah A.M.M.S., Matarazzo D., Tool-wear Prediction and Pattern-recognition Using Artificial Neural Network and DNA-based Computing, Journal of Intelligent Manufacturing, 28, 6, pp. 1-17, (2017)
  • [3] Pratama M., Dimla E., Lai C.Y., Et al., Metacognitive Learning Approach for Online Tool Condition Monitoring, Journal of Intelligent Manufacturing, 4-5, pp. 1-21, (2017)
  • [4] Jain A.K., Lad B.K., A Novel Integrated Tool Condition Monitoring System, Journal of Intelligent Manufacturing, 3, pp. 1-14, (2017)
  • [5] Zerehsaz Y., Shao C., Jin J., Tool Wear Monitoring in Ultrasonic Welding Using High-order Decomposition, Journal of Intelligent Manufacturing, (2016)
  • [6] Liang S.Y., Hecker R.L., Landers R.G., Machining Process Monitoring and Control: the State-of-the-art, Journal of Manufacturing Science and Engineering, 126, 2, pp. 297-310, (2004)
  • [7] Jeon J.U., Kim S.W., Optical Flank Wear Monitoring of Cutting Tools by Image Processing, Wear, 127, 2, pp. 207-217, (1988)
  • [8] Karthik A., Chandra S., Ramamoorthy B., Et al., 3D Tool Wear Measurement and Visualization Using Stereo Imaging, International Journal of Machine Tools & Manufacture, 37, 11, pp. 1573-1581, (1997)
  • [9] D'addona D.M., Teti R., Image Data Processing via Neural Networks for Tool Wear Prediction, Procedia Cirp, 12, pp. 252-257, (2013)
  • [10] Xie C., Yu S., Xu Z., Et al., Research on Remote Monitoring System of CNC Machine Tool Based on OPC UA, Mechanical Design & Manufacturing Engineering, 46, 12, pp. 51-53, (2017)
12