Milling tool wear prediction using multi-sensor feature fusion based on stacked sparse autoencoders

Tool wear prediction was significant for improving processing efficiency, ensuring product quality and reducing tool costs in manufacturing. In this paper, a novel deep learning method based on stacked sparse autoencoders (SSAE) and multi-sensor feature fusion was proposed for milling tool wear prediction. The signal features were extracted in time, frequency and time-frequency domains and the optimum multi-sensor features were determined by correlation analysis, which were input into the SSAE for deep feature learning. Backpropagation neural network (BPNN) was utilized to establish the prediction model of tool wear. Different milling wear experiment datasets were applied to verify the predictive performance of the trained models. The prediction results showed that the proposed model had the minimum root mean square error (RMSE) and maximum coefficient of determination (R2), which outperformed the comparative predictive models. The combination of multi-sensor feature fusion and deep learning method was demonstrated for improving the predictive performance.

Automated summary

A novel deep learning method based on multi-sensor feature fusion was proposed for milling tool wear prediction. The method extracted signal features in different domains and used correlation analysis to determine the optimal features. Experimental results showed that the proposed method outperformed other comparative methods in predictive performance.