Relationship analysis between chaotic characteristics of acoustic emission signal and tool wear condition

The paper is mainly aimed at building a method to monitor tool wear condition based on analysis of chaotic characteristics of acoustic emission (AE) signal generated during tool cutting process. First, according to the AE signals from different tool cutting periods, multiple chaos descriptions, such as reconstructing the strange attractor track and Poincare map, computing correlation dimension and the max Lyapunov exponent, are used to prove the existing of chaotic characteristics in the series. Then, use the least square method to fit a curve to the computed characteristics points, including the correlation dimension and the max Lyapunov exponent. Furthermore, the developing trend of these two parameters is discussed. The results show that chaotic phenomena exist in the acoustic emission signal, and the chaotic characteristics, like correlation dimension and the max Lyapunov exponent, will change with the development of tool wear process. Therefore relationship analysis between chaotic characteristics of AE series and the tool wear condition may provide a new path for online monitoring of tool cutting process.