Cutting tool operational reliability prediction based on acoustic emission and logistic regression model

Abstract

Working status of cutting tools (CTs) is crucial to the products' precision. If broken down, it may lead to waste product. Condition monitoring and life prediction are beneficial to the manufacturing process. In this research, Logistic regression models (LRMs) and acoustic emission (AE) signal are used to evaluate reliability. Based on different conditions estimation, CTs are investigated to determine the best maintenance time. Based on experimental data analysis, AE and cutting force signals have better linear relationship with CT wearing process. They can be used to demonstrate CT degradation process. Frequency band energy is determined as characteristic vector for AE signal using wavelet packet decomposition. Two reliability estimation models are constructed based on cutting force and AE signals. One uses both signals, while the other uses only AE signal. The reliability degree can be estimated using the two models, independently. AE feature extraction and LRM can effectively estimate CT conditions. As it is difficult to monitor cutting force in a practical working condition, it is an effective method for CT reliability analysis by the combination of AE and LRM method. Experimental investigation is used to verify the effectiveness of this method.