Tool Wear Condition Monitoring for Drilling CFRP/TC4 Laminated Materials Using scSE Optimised ResNet-50
Aiming at the problems of severe tool wear in the preparation of assembly holes for laminated materials consisting of carbon fibre reinforced composites and titanium alloys,a tool wear monitoring method with scSE optimised deep residual neural network(ResNet-50)was proposed.Drilling experiments were carried out to collect force and temperature signals during the drilling processes.The signals were converted to wavelet scale spectrum using continuous wavelet transform.The ResNet-50 network structure was built to calibrate the weights of the convolutionally extracted feature maps from both spatial and channel dimensions.The results show that scSE may enhance the useful features and suppress the useless features from both spatial and channel dimensions,and the recognition accuracy of the network structure optimised by scSE reaches 96.15％.
万方数据
维普
