In-process tap tool wear monitoring and prediction using a novel model based on deep learning

Abstract

Tool wear monitoring and prediction are of great importance for machining precision and surface integrity. In order to ensure good quality of the machining components, tool wear should be monitored and predicted in time. However, the traditional methods greatly depend on feature selection and extraction, and accuracy and generalization are limited. In this paper, a novel model based on deep learning was proposed to monitor and predict tap tool wear. Firstly, 1D CNN was designed to extract features from the vibration data, and then the residual block with the dilated convolution was specially developed to accept the features from 1D CNN. After that, the fully connected neural networks were designed to predicate tool wear. Moreover, to verify the superiority and generalization of the proposed method, the taping experiment from a real engine cylinder head production line was conducted. During the tapping process, the vibration signal and tool wear were collected. And then the training experiment was conducted with the collected data. The prediction results with the proposed model were compared with those of the state-of-the-art algorithms. The compared results showed that the proposed model was more robust and accurate for tool wear prediction.