机械科学与技术2024，Vol.43Issue(8)：1403-1410,8.DOI:10.13433/j.cnki.1003-8728.20230108
采用残差结构和卷积神经网络的铣刀磨损研究
Research on Milling Cutter Wear Using Residual Structure and Convolution Neural Network
摘要
Abstract
To solve the problem of low accuracy of traditional convolutional neural network and improve the accuracy of tool wear monitoring,a one-dimensional convolutional neural network based on residual structure was proposed in this paper.Two residual blocks were used in the model,and the residual structure can skip a part of convolution layer to reduce the training time of the model,and keep the information to connect with the output of the next layer.The collected information is multidimensional,and the convolutional neural network can adaptively extract relevant features,which is more reliable than traditional machine learning methods depending on manual experience to extract features.The experimental results show that the convolutional neural network with residual structure has lower loss function value than the traditional convolutional neural network,and it also has a good performance in accuracy,which improves the classification accuracy of tool wear monitoring.关键词
一维卷积/残差结构/刀具磨损监测/机器学习Key words
one-dimensional convolutional/residual structure/tool wear monitoring/machine learning分类
信息技术与安全科学引用本文复制引用
程胜明,王雅君,张昕晨,冷峻宇..采用残差结构和卷积神经网络的铣刀磨损研究[J].机械科学与技术,2024,43(8):1403-1410,8.基金项目
辽宁省教育厅项目(LJKZ0532) （LJKZ0532）
