采用深度学习的铣刀磨损状态预测模型

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

摘要/Abstract

摘要： 为提高刀具磨损监测的预测精度与泛化性能，研究了基于深度学习的铣刀磨损状态预测，提出了基于堆叠稀疏自动编码网络与卷积神经网络的两种预测模型。堆叠稀疏自动编码网络对特征向量进行降维并将其纳入分类器来实现预测，可避免特征选择对先验知识的依赖；卷积神经网络将铣削振动数据转化为小波尺度图并输入模型完成分类，精简了传统建模流程。最后将提出的两种模型与传统神经网络模型进行比较，验证了所提模型的效率与精度。

关键词: 刀具磨损, 小波变换, 自动编码器, 深度学习

Abstract: 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.

Key words: tool wear, wavelet transform, autocoder, deep learning

中图分类号:

TP391
TH164

戴稳1;张超勇1;孟磊磊1;薛燕社1;肖鹏飞1;尹勇2. 采用深度学习的铣刀磨损状态预测模型[J]. 中国机械工程, DOI: 10.3969/j.issn.1004-132X.2020.17.009.

DAI Wen1;ZHANG Chaoyong1;MENG Leilei1;XUE Yanshe1;XIAO Pengfei1;YIN Yong2. Prediction Model of Milling Cutter Wear Status Based on Deep Learning[J]. China Mechanical Engineering, DOI: 10.3969/j.issn.1004-132X.2020.17.009.

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链接本文: http://www.cmemo.org.cn/CN/10.3969/j.issn.1004-132X.2020.17.009

http://www.cmemo.org.cn/CN/Y2020/V31/I17/2071

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