机械科学与技术2024，Vol.43Issue(4)：667-673,7.DOI:10.13433/j.cnki.1003-8728.20220269
采用门控循环单元神经网络和多特征融合的铣削刀具磨损监测
Milling Tool Wear Monitoring by Using Gated Recurrent Unit Neural Network and Multi-feature Fusion
摘要
Abstract
To realize the tool wear condition monitoring in the production of a vehicle engine's cylinder head and to enhance the computational efficiency and recognition accuracy of tool wear monitoring,a tool condition monitoring method based on the gated recurrent unit neural network and the multi-feature fusion method is proposed for identifying the width of milling tool flank wear.The effectiveness of the proposed method is verified with the milling force signal data,and the effects of different hyper-parameter settings on the model recognition accuracy is analyzed.The optimal hyper-parameters are given;the accurate recognition of milling tool wear is realized.关键词
刀具磨损/铣削力信号/状态监测/门控循环单元神经网络Key words
tool wear/milling force signal/condition monitoring/GRU neural network分类
机械制造引用本文复制引用
葛慧,赵金富,韩林池,麻俊方,宋清华,王润琼,刘战强,杜宜聪,王兵,蔡玉奎..采用门控循环单元神经网络和多特征融合的铣削刀具磨损监测[J].机械科学与技术,2024,43(4):667-673,7.基金项目
国家自然科学基金项目(51922066,51875320) （51922066,51875320）
