Prediction Model of Milling Cutter Wear Status Based on Deep Learning

被引：4

作者：

Dai W. ^{[1
]}

Zhang C. ^{[1
]}

Meng L. ^{[1
]}

Xue Y. ^{[1
]}

Xiao P. ^{[1
]}

Yin Y. ^{[2
]}

机构：

[1] State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan

[2] Hubei Key Laboratory of Digital Manufacturing, Wuhan University of Technology, Wuhan

来源：

Zhongguo Jixie Gongcheng/China Mechanical Engineering | 2020年 / 31卷 / 17期

关键词：

Autocoder; Deep learning; Tool wear; Wavelet transform;

D O I：

10.3969/j.issn.1004-132X.2020.17.009

中图分类号：

学科分类号：

摘要：

In order to improve the prediction accuracy and generalization performance of tool wear monitoring, the milling tool wear state prediction was studied based on deep learning. Two prediction models were proposed based on stacked sparse auto-encoder network and convolutional neural network. The stack sparse auto-encoder network used dimensionality reduction processing of feature vectors and incorporated them into the classifier to achieve classification prediction, avoiding the dependence on prior knowledges in feature selection. Convolutional neural networks completed the conversion of milling vibration data into wavelet scale maps as model inputs, and greatly simplified the traditional modeling processes. Finally, the two proposed models were compared with traditional neural network models to verify the efficiency and accuracy of the proposed models. © 2020, China Mechanical Engineering Magazine Office. All right reserved.

引用

页码：2071 / 2078

页数：7

共 17 条

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