In this paper, monitoring and prediction of cutting tool wear condition based on dynamic data driven approaches were investigated. Sensor signals obtained from the machining processes were processed through wavelet denoising to filter the noise un-related to cutting, features in time and frequency domains were extracted using classical signal processing approaches, and then were selected with Pearson correlation coefficient. The most related features were sent to the feature fusion approaches including neural network (NN), adaptive neural fuzzy inference system (ANFIS), or support vector regression (SVR) to estimate the tool wear. Statistics performance evaluation based on correlation coefficient (R2), average absolute error (AAE), and Se/Sy, as well as cross validation, selected the most proper feature fusion approach. Further, prediction models based on Bayesian model average were applied to predict the future tool wear. A case study based on the end mill experiment with signals of 3-axis cutting forces, 3-axis vibrations and acoustic emission, illustrated the proposed approach. It showed that ANFIS has the best estimation accuracy with the R2 of 0.99, AAE of 0.42, Se/Sy of 0.12, and cross validation error of 13.36. In the prediction stage, the prediction model has high prediction accuracy with all the experiment results covered by 95% confidence interval of prediction.
Features of Cross-Correlation Analysis in a Data-Driven Approach for Structural Damage Assessment