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
葛慧 1韩林池 1麻俊方 1宋清华 2王润琼 2刘战强 2杜宜聪 2王兵 2蔡玉奎 2赵金富2
作者信息
- 1. 中国重汽集团济南动力有限公司,济南 250220
- 2. 山东大学 机械工程学院,济南 250061
- 折叠
摘要
为实现汽车发动机缸盖生产中刀具磨损状态的监测,提高刀具磨损监测方法的计算效率和识别精度,基于门控循环单元神经网络和多特征融合方法提出了面向铣刀后刀面磨损带宽度识别的刀具状态监测方法.通过铣削力信号数据对所提出方法的有效性进行了验证,分析了不同超参数设置对模型识别精度的影响机制,给出了最优超参数,实现了对铣削刀具磨损的精确识别.
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引用本文复制引用
基金项目
国家自然科学基金(51922066)
国家自然科学基金(51875320)
出版年
2024
国家科技期刊平台
NSTL
维普
万方数据