机械科学与技术2025，Vol.44Issue(11)：1912-1918,7.DOI:10.13433/j.cnki.1003-8728.20230357
改进HPO优化VMD-GRU法在刀具磨损状态识别中的应用
Application of Improved HPO Optimized VMD and GRU Method in Tool Wear State Recognition
摘要
Abstract
In order to improve the accuracy of the tool wear state identification during machining,a recognition model based on improved Hunter-prey optimizer(HPO),variational mode decomposition(VMD)and gated recurrent unit(GRU)neural network is proposed in this paper.Firstly,the HPO improved by Tent chaos and Levy flight strategy was used to optimize VMD to determine the best combination of the decomposition layers and the penalty factor.Then,the features of the original current signal and the decomposed signal are extracted and the kernel principal component analysis(KPCA)method is used to reduce the feature dimension.Finally,the dimensionality reduction features were input into the Gated Recurrent Unit(GRU)neural network model to realize the recognition of tool wear states.The experimental results show that the recognition model proposed in this paper has higher recognition accuracy and efficiency than other models,and has higher universality.关键词
刀具磨损识别/电流信号/变分模态分解/猎人猎物算法/门控循环单元Key words
tool wear recognition/current signal/variational mode decomposition/hunter-prey optimizer/gated recurrent unit分类
机械制造引用本文复制引用
韩宁,李国富,任潞..改进HPO优化VMD-GRU法在刀具磨损状态识别中的应用[J].机械科学与技术,2025,44(11):1912-1918,7.基金项目
国家自然科学基金项目(22108316) （22108316）
