A new time-space attention mechanism driven multi-feature fusion method for tool wear monitoring

In order to accurately monitor the tool wear process, it is usually necessary to collect a variety of sensor signals during the cutting process. Different sensor signals can provide complementary information in the feature space. In addition, monitoring signals are time series data, which also contains a wealth of time dimension tool degradation information. However, how to fuse multi-sensor information in time and space dimensions is a key issue that needs to be solved. In this paper, a new time-space attention mechanism driven multi-feature fusion method is proposed for tool wear monitoring and residual useful life (RUL) prediction. A time-space attention mechanism is innovatively introduced into the tool wear monitoring model, and features are weighted from two dimensions of space and time. It can more accurately capture the complex spatio-temporal relationship between tool wear values and features, so that the model can accurately predict wear values even if it gives up cutting force signals with good trends. The experimental results show that the correlation of the predicted wear and the actual wear is greater than 0.95, and the relative accuracy of the RUL predicted by the predicted wear combined with the particle filter can also be around 0.78. Compared with other feature fusion models, the proposed method realizes the tool wear monitoring more accurately and has better stability.

Automated summary

This paper proposes a new method for tool wear monitoring and residual useful life (RUL) prediction, which effectively captures the complex relationship between tool wear values and features by introducing a time-space attention mechanism and feature weighting. It achieves more accurate wear monitoring and stable prediction results.