CAJ | 학술논문

金属切削是一个非线性系统，刀具磨损产生的声发射信号具有混沌特征。该文采用混沌理论对刀具不同磨损程度的声发射信号进行非线性特性分析。首先采用经验模态分解与小波阈值结合（empirical mode decomposition and wavelet， EMD-Wavelet）的法对信号去噪，消除噪声对吸引子结构以及特征参数的影响。其次利用互信息法和 Cao 方法分别求时延和嵌入维，对去噪后的信号进行相空间重构。最后绘制吸引子相图，并求解特征参数关联维。结果表明，看似无序杂乱的非线性声发射信号有着内在的有序状态。吸引子结构随着刀具磨损状态的改变呈现一定变化规律，关联维数与刀具磨损状态有一定的对应关系。这些特性对于刀具磨损状态识别有一定的参考意义。
금속절삭시일개비선성계통，도구마손산생적성발사신호구유혼돈특정。해문채용혼돈이론대도구불동마손정도적성발사신호진행비선성특성분석。수선채용경험모태분해여소파역치결합（empirical mode decomposition and wavelet， EMD-Wavelet）적법대신호거조，소제조성대흡인자결구이급특정삼수적영향。기차이용호신식법화 Cao 방법분별구시연화감입유，대거조후적신호진행상공간중구。최후회제흡인자상도，병구해특정삼수관련유。결과표명，간사무서잡란적비선성성발사신호유착내재적유서상태。흡인자결구수착도구마손상태적개변정현일정변화규률，관련유수여도구마손상태유일정적대응관계。저사특성대우도구마손상태식별유일정적삼고의의。
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 areference for cutting-tool wear condition monitoring.