A Sequence-to-Sequence Model With Attention and Monotonicity Loss for Tool Wear Monitoring and Prediction
Gang Wang, Feng Zhang
Abstract
Recently, deep learning has been successfully applied in tool wear monitoring system. However, since the tool wear accumulates in the cutting process, the state of the cutting tool shows a degradation trend, which has not been fully exploited by the current deep learning models. In this paper, an end-to-end deep learning model, named Sequence-to-Sequence Model with Attention and Monotonicity Loss (SMAML) is proposed to simultaneously monitor and predict the tool wear. In the proposed SMAML, the encoder is utilized to extract features from signals collected by different sensors using the group convolution, while the decoder is designed to produce sequential outputs including the results of tool wear monitoring and multi-step-ahead prediction. Besides, a monotonicity loss function is proposed to capture the degeneration characters in these sequential outputs. The experiments are conducted on real-world datasets, which were collected from a high-speed CNC machine. The experimental results demonstrate the effectiveness of the proposed model which outperforms other conventional machine learning and deep learning models.