Tool wear monitoring and prediction based on sound signal

Milling plays a core role in the manufacturing industry. If a milling tool suffers severe wear or breakage during the manufacturing process, it requires immediate attention to prevent precision errors and poor surface quality. Previous studies applied multiple sensors, such as force sensors, microphones, and acoustic emission sensors, to extract the features related to tool wear. However, owing to the complex mechanism causing tool wear, the results of these studies were not only quantitatively different but also varied with respect to cutting conditions. Therefore, this research discussed the relationship between sound signal and tool wear under multiple cutting conditions. Collinearity diagnostics and a stepwise regression procedure were used to optimize the time–frequency statistic features generated from the wavelet packet decomposition. The regression and an artificial neural network model were developed to predict the degree of tool wear. The proposed method provided solid statistical support for the feature selection process, and the results showed that it will maintain prediction accuracy regardless of the different cutting conditions. Further, the method achieved better accuracy than the commonly used root mean square value.