Tool Condition Monitoring Based on Fusion Feature and Attention Network
Tool wear is one of the important factors affecting the machining quality of parts.In order to ac-curately and reliably monitor the tool wear state,a tool state prediction model based on fusion feature and attention mechanism convolutional neural network is proposed.Firstly,cutting force and vibration signals are collected in the process of milling to extract effective machining signals.Secondly,the force and vibra-tion signals are reconstructed by signal processing techniques,such as symmetric dot pattern and wavelet packet decomposition,to establish two-dimensional diagrams of signal characteristics in different wear States,and different process parameters are characterized by gray scale.Finally,the convolution neural net-work model of SE attention mechanism is constructed,and the tool wear state is predicted based on the fused signal feature map and process parameter gray map.The results show that the recognition model based on fusion features and attention network has a good recognition effect on the prediction of tool wear state.
万方数据
维普
