基于带阈值模块一维残差网络的刀具磨损监测方法

doi:10.3969/j.issn.1000-1158.2022.04.11

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Abstract A tool wear monitoring model based on one-dimensional residual network with threshold module and a tool wear prediction model based on bidirectional long short-term memory network are designed. The sensor signal is input into the monitoring model after wavelet decomposition, the threshold module automatically selects the threshold to reduce the noise of the signal and the residual module extracts the signal characteristics, then outputs the tool wear monitoring value, and inputs it into the prediction model to obtain the tool wear prediction value. The experimental results show that the monitoring accuracy of this monitoring model is improved by 0.327% and 1.697% respectively compared with the one-dimensional residual network model without threshold module and convolution neural network model; the prediction effect of the prediction model is good, and the absolute error is only 0.024.

Key words： metrology tool wear residual networks threshold module long short-term memory network

Received: 14 January 2021 Published: 08 April 2022

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TB93

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	Articles by authors
	GUO Bao-su
	HAN Tian-jie
	ZHANG Yu
	WU Feng-he

Cite this article:

GUO Bao-su,HAN Tian-jie,ZHANG Yu, et al. Tool Wear Monitoring Method Based on One-Dimensional Residual Network with Threshold Module[J]. Acta Metrologica Sinica, 2022, 43(4): 501-506.

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http://jlxb.china-csm.org:81/Jwk_jlxb/EN/10.3969/j.issn.1000-1158.2022.04.11 OR http://jlxb.china-csm.org:81/Jwk_jlxb/EN/Y2022/V43/I4/501

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