Volume 1 Issue 2
Apr.  2021
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Dianfang MU, Xianli LIU, Caixu YUE, Qiang LIU, Zhengyan BAI, Steven Y. LIANG, Yunpeng DING. On-line tool wear monitoring based on machine learning[J]. Journal of Advanced Manufacturing Science and Technology , 2021, 1(2): 2021002. doi: 10.51393/j.jamst.2021002
Citation: Dianfang MU, Xianli LIU, Caixu YUE, Qiang LIU, Zhengyan BAI, Steven Y. LIANG, Yunpeng DING. On-line tool wear monitoring based on machine learning[J]. Journal of Advanced Manufacturing Science and Technology , 2021, 1(2): 2021002. doi: 10.51393/j.jamst.2021002

On-line tool wear monitoring based on machine learning

doi: 10.51393/j.jamst.2021002
Funds:

This research was funded by Projects of International Cooperation and Exchanges NSFC (Grant Number 51720105009) and National Key Research and Development Project (Grant Number 2019YFB1704800) and Outstanding Youth Fund of Heilongjiang Province (Grant Number YQ2019E029) and Natural Science Foundation for Colleges and Universities of Jiangsu Province (No. 19KJB460021)

  • Received Date: 2021-02-02
  • Rev Recd Date: 2021-02-18
  • Available Online: 2021-03-05
  • Publish Date: 2021-03-04
  • Accurate tool condition monitoring is necessary for the development of automatic milling technology. In order to improve the accuracy and real-time of online monitoring of tool wear state in machining process, an online monitoring system of milling cutter state based on LabVIEW software development is proposed. Firstly, the modern monitoring technology is introduced into the online monitoring of tool state in principle. The vibration signal is analyzed by wavelet packet in time-frequency domain, and the online monitoring of tool state is realized by machine learning algorithm model. The system can be used for real-time monitoring of tool status, timely alarm to facilitate tool replacement, and ensure high efficiency and high quality of processing. The effectiveness and feasibility of the online monitoring system for milling cutter wear state are verified by experiments, and the purpose of online monitoring tool wear state is preliminarily realized.

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