关键词: 刀具磨损类型, 卷积神经网络, 模型预训练, 图像识别

Abstract: Effective identification of tool wear and wear type is an important means to monitor machining status.Aiming at the lack of manual participation in the identification of conventional tool wear types,an intelligent recognition method based on convolutional neural network tool wear types was proposed.By analyzing the tool wear type and wear process,the network structure for identifying the wear state of the tool was designed.The network model was pre-trained by convolution auto-encoder,and the parameters were fine-tuned by backpropagation algorithm combined with adaptive moment estimation algorithm.The experiment proved that the average recognition rate of the model for tool wear type reached 96.25%.A method for automatic detection of tool wear value based on tool wear type identification was proposed.The experimental results showed that the error rate of the test method was less than 10%,and the average error rate was 5.93%,which could meet the practical application requirements.

Key words: tool wear type, convolutional neural networks, model pre-training, image recognition