宇航材料工艺2024，Vol.54Issue(5)：40-49,10.DOI:10.12044/j.issn.1007-2330.2024.05.005
基于深度学习的CFRP/TC4叠层结构制孔刀具磨损状态监测
Tool Wear Condition Monitoring Based on Deep Learning During Drilling CFRP/TC4 Laminated Structure
摘要
Abstract
Due to excellent physical and mechanical properties,carbon fiber reinforced plastics(CFRP)and titanium alloys(TC4)were often widely used in the aerospace industry as laminated structures.Since CFRP and TC4 were both typical difficult-to-machine materials,and had different mechanical and thermal properties,the tool wear was rapid during the hole-making process,which affected the machining quality.In order to ensure the quality of drilling and timely replacement of cutting tools,a tool wear condition monitoring model based on convolution neural network-long short time memory(CNN-LSTM)was established.The model took the feature of force and acoustic emission signals with strong correlation to tool wear as input and the tool wear condition labels as output to realize tool wear monitoring.The results show that the model has an accuracy rate of 97.222％,which can effectively monitor the tool wear status during the drilling process of CFRP/TC4 laminated structures.关键词
刀具磨损状态监测/CFRP/TC4叠层/特征提取/深度学习Key words
Tool wear condition monitoring/CFRP/TC4 laminated structures/Feature extraction/Deep learning分类
矿业与冶金引用本文复制引用
江庆泉,李鹏南,邱新义,李树健,王春浩..基于深度学习的CFRP/TC4叠层结构制孔刀具磨损状态监测[J].宇航材料工艺,2024,54(5):40-49,10.基金项目
国家自然科学基金(52275423,52105442,51975208) （52275423,52105442,51975208）
