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On-line tool wear monitoring based on machine learning

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.