In-process tool condition monitoring based on convolution neural network

被引：0
|
作者：
Cao D. [1 ]
Sun H. [1 ]
Zhang J. [1 ]
Mo R. [1 ]
机构：
[1] Key Laboratory of High Performance Manufacturing for Aero Engine, Ministry of Industry and Information Technology, Northwestern Polytechnical University, Xi'an
来源：
Jisuanji Jicheng Zhizao Xitong/Computer Integrated Manufacturing Systems, CIMS | 2020年 / 26卷 / 01期
关键词：
Convolution neural network; Feature extraction; Signals in time domain; Tool condition monitoring; Tool wear condition;
D O I：
10.13196/j.cims.2020.01.008
中图分类号：
学科分类号：
摘要：
To improve the accuracy and generalization, a tool condition monitoring approach is proposed based on convolutional neural network(CNN). To prevent the loss of signal information caused by the data preprocessing, signals in time domain were used to analyze tool wear condition quantitatively. Instead of manually extracting features from signals, an adaptive method is developed by using deep network. In order to mine tiny features, deeper neural network is used. The experiment study verifies the approach's excellent performance. Both accuracy and generalization are improved, when the limitation of manual feature extraction is avoided. The comparison with relevant studies also validates its feasibility and efficiency. © 2020, Editorial Department of CIMS. All right reserved.
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页码：74 / 80
页数：6
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