Chaotic characteristic analysis of tool wear acoustic emission signal

Abstract: In metal cutting process, surface quality and dimensional accuracy of the work piece is affected by cutting-tool wear condition. So it is important to study the cutting-tool wear, especially in automation production. Cutting-tool wear is a complex process; it is affected by various factors like cutting parameters, material characteristics and cutting environment, etc. Metal cutting is a nonlinear system; there are a lot of non-stationary signals used in condition monitoring and fault diagnosis. Vibration, force and acoustic emission (AE) are the typical signal type widely used in cutting-tool wear research. In this paper, we chose AE signal to be the carrier in analyzing cutting-tool wear. AE is the class of phenomena where transient elastic waves are generated by the rapid release of energy when the materials are distorted or under the outside load. The AE signal produced by cutting -tool wear is high-frequency and the bandwidth is nearly 50 kHz-1 MHz, so it can weaken the influence of low-frequency noise like mechanical noise and ambient noise. The measured signal sometimes contains high-frequency noise. In this paper, chaos theory was used in analyzing the nonlinear characteristics of the AE signal. Chaos theory is sensitive to noise; therefore, noise reduction was done with the method based on empirical mode decomposition and wavelet (EMD-Wavelet) before computing. The signal were decomposed into several intrinsic mode functions which was from high-frequency to low-frequency by use of EMD, then it was used to determine the noise dominated intrinsic mode functions based on consecutive mean square error (CMSE) proposed by Boudraa and then restrained them. A new signal were reconstructed by adding the rest intrinsic mode functions together and a further and last de-noising was using wavelet to processing the new one in order to get more pure signal. Before extracting the chaotic character, an important step was to reconstruct a phase space from the de-noised signal. To get the phase space vector, two key parameters, delay time and embedding dimension, had to be determined. Method based on mutual-information was utilized in computing delay time and Cao method for embedding dimension. After reconstructing the phase space, the chaos attractor was presented which can obviously reflect the cutting-tool wear condition. The structure of the attractor differed with tool wear. In order to prove the effect of noise reduction, a comparison was done between attractors another one reconstructed from original signal. The attractor reconstructed from the purified signal was smoother than the noisy signal. To get accurate result, the correlation dimension was computed. The result showed that seemingly random AE signal has internal ordered state and there was a certain correspondence between the correlation dimension and tool wear. So the chaos character can be used in cutting-tool wear identification and the result can offer a reference for cutting-tool wear condition monitoring.