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Open Access

On-line tool wear monitoring based on machine learning

Dianfang MUa, Xianli LIUa( ), Caixu YUEa, Qiang LIUa, Zhengyan BAIa, Steven Y. LIANGb, Yunpeng DINGc
Key Laboratory of Advanced Manufacturing and Intelligent Technology, Ministry of Education, Harbin 150080, China
George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta 30332, USA
Department of Precision Manufacturing Engineering, Suzhou Vocational Institute of Industrial Technology, Suzhou 215104, China

Peer review under responsibility of Editorial Committee of JAMST

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Abstract

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.

Keywords

Tool condition monitoringTool wearVibration signalLabVIEW

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Journal of Advanced Manufacturing Science and Technology
Volume 1 Issue 2,
April 2021
DOI: 10.51393/j.jamst.2021002

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Cite this article:
MU D, LIU X, YUE C, et al. On-line tool wear monitoring based on machine learning. Journal of Advanced Manufacturing Science and Technology, 2021, 1(2): 2021002. https://doi.org/10.51393/j.jamst.2021002

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Received: 02 February 2021
Revised: 18 February 2021
Accepted: 27 February 2021
Published: 15 April 2021
© 2021 JAMST All rights reserved.

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)，which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.