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| Tool Wear Condition Monitoring Based on Convolution-gated Recurrent Neural Network |
| WU Feng-he1,2,ZHONG Hao1,ZHANG Qin1,GUO Bao-su1,2,SUN Ying-bin1,2 |
1. Mechanical College,Yanshan University,Qinhuangdao,Hebei 066004,China
2. Heavy Intelligent Manufacturing Equipment Technology Innovation Center of Hebei Province,Qinhuangdao, Hebei 066004, China |
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Abstract Aiming at the demand of online monitoring tool wear status, a method for online monitoring tool wear status based on convolution gated recurrent neural network was proposed. Based on the advantages of convolutional neural network and gated recurrent unit neural network, a convolutional gated recurrent neural network was constructed; the cutting force was used as the input signal and the noise was removed by wavelet noise reduction; the convolutional neural network was used to extract the high-dimensional features that represented the key information of the tool wear status; the cumulative effect of the model on the time scale was fully expressed through the gated recurrent neural unit, which reflected the wear time-series characteristics. Experiments showed that under the condition of limited tool wear data sample, the tool wear state monitoring through convolution-gated recurrent neural network had a good effect, and the accuracy rate was 97%.
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Received: 16 December 2019
Published: 20 August 2021
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2021, Vol. 42 
