Tool Wear Prediction Approach Based on Power Sensor

Abstract:

The power sensor was used to monitor machine processing power which was more practical and of no influence on the cutting process in comparison with conventional sensors such as force and AE. For the collected power signal, based on the analysis of signal features, a Reprocessing Sparse Bayesian Learning(RPSBL) with Nondominated Sorting Genetic Algorithm Ⅱ(NSGAII) approach was proposed to achieve the tool wear prediction. First, the features reprocessing was applied to eliminating impacts caused by power fluctuation and other casual factors, and the sensitivity of tool wears enhanced. Then, the tool wear was predicted by Sparse Bayesian Learning based on the reprocessed features. Finally, the parameter of Sparse Bayesian Learning was also optimized by NSGAII to improve the prediction accuracy. The experimental results on a milling machine tool show the effectiveness in predicting the tools wear by the proposed approach. A comparative study of different methods shows feature sensitivity enhancement of the tool wear by feature reprocessing ensures its prediction accuracy; Prediction accuracy can be further improved and the maximum of the prediction error can be minimized through the optimization of SBL with NSGAII.