Tool wear monitoring based on deep learning

被引：0

作者：

Zhang C. ^{[1
]}

Yao X. ^{[1
]}

Zhang J. ^{[1
]}

Liu E. ^{[1
]}

机构：

[1] School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou

来源：

Jisuanji Jicheng Zhizao Xitong/Computer Integrated Manufacturing Systems, CIMS | 2017年 / 23卷 / 10期

基金：

中国国家自然科学基金;

关键词：

Convolutional neural network; Data acquisition; Deep learning; Tool wear monitoring; Wireless triaxial accelerometer;

D O I：

10.13196/j.cims.2017.10.008

中图分类号：

学科分类号：

摘要：

To monitor the tool wear for machining equipment in manufacturing workshops, deep learning was proposed to realize the tool wear monitoring. As the latest research result in Artificial Intelligence (AI) field, the Convolutional Neural Network (CNN) was adopted to build the model of tool wear monitoring. A flow chart of tool wear monitoring was given, and a micro milling machine and a wireless triaxial accelerometer were used to build the experimental setup to acquire measurement data. The experimental results showed that the proposed approach was simple to realize the tool wear monitoring with higher accuracy and lower loss during the learning process by comparing with other two common models that were deep CNNs and traditional Neural Network (NN), and a classification of tool wear degree was realized. © 2017, Editorial Department of CIMS. All right reserved.

引用

页码：2146 / 2155

页数：9

共 29 条

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