Search

Copyright of Control Theory & Applications / Kongzhi Lilun Yu Yinyong is the property of Editorial Department of Control Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Cutting is an important process in machining. In order to improve the automatic and intelligent level of machining and improve the production efficiency and quality, it is urgent to monitor the tool wear state. The feature extraction of wear state is the key to the tool wear monitoring. In view of the unique advantages of multifractal theory in accurately depicting the nonlinear phenomena and processes of the system, a tool wear state recognition method based on multifractal detrended fluctuation analysis (MF-DFA) and least squares support vector machine (LS-SVM) is proposed. The acoustic emission (AE) signal is denoised with wavelet packet analysis, and the best tree of wavelet packet decomposition is determined and reconstruction is performed based on the minimum Shannon criterion so as to achieve the purpose of signal initial denoising. Firstly, the MF-DFA method is used to deal with the noise emission signals of the tool wear after denoising, and the long range correlation and fractal characteristics are discussed. It shows that the tool wear time sequence is an orderly process with long range correlation, and the internal fluctuation is not random, and it has the ability to maintain the trend. Then, the multifractal spectrum parameters of different wear stages were analyzed and compared. The parameters of singular exponent corresponding to the point of extreme value and multifractal spectrum width are increasing with the progression of the wear stage, which indicates that the greater the wear amount, the greater the fluctuation of the AE signal, the more uneven the probability measurement of the whole fractal structure, the more random the fluctuation. The values of the AE signal multifractal dimension under different wear states are less than zero, and the multifractal spectrum is left hook like, indicating the number of the maximum subset in the probability measure is relatively large. The absolute value of the normal wear stage is the smallest, which indicates that the volatility is the smallest in this stage; the value of the parameter increases with the increase of the wear amount, indicating that the greater the fluctuation degree of generalized Hurst exponent, the stronger the multifractal characteristics. The singular exponent corresponding to the point of extreme value, the multifractal spectrum width and the mean of the generalized Hurst exponent, which can sensitively characterize the tool wear state, were selected as the characteristic quantities, and the three-dimensional feature vectors were constructed to characterize the tool wear stage. The clustering effect of the extracted tool wear state characteristics was obvious. The LS-SVM algorithm, SVM algorithm and BP (back propagation) neural network are applied to recognize the tool wear state. Simplex iterative algorithm is used to optimize the parameters, the optimal model is constructed to determine the performance of each group of parameters, and the parameters of regularization and kernel function are determined. The average recognition accuracy is 97.78%. The results show that the tool wear AE signal has long range correlation and obvious multifractal characteristics, the multifractal parameters, i.e. singular exponent corresponding to the point of extreme value, multifractal spectrum width and mean of the generalized Hurst exponent can be used as sensitive characterization for the feature of tool wear stage, and the tool wear stages can be clearly distinguished. The multifractal spectrum features extracted with the method based on MF-DFA and LS-SVM can identify the different wear stages of the tool well, verify the effectiveness of the recognition method, improve the accuracy of recognition, and lay a foundation for the realization of the wear prediction. [ABSTRACT FROM AUTHOR]

Tool is the most common part easy to wear and fail in CNC (computer numerical control) system, machining center and other highly integrated and intelligent equipment. Tool wear condition monitoring is of great significance to ensure the machining quality, machining accuracy and machining efficiency of the parts. At present, the common tool wear condition monitoring signal is vibration signal, cutting force signal, current signal and acoustic emission signal. Compared with other monitoring signals, the frequency of acoustic emission signal can reach 50 kHz - 1 MHz with high sensitivity and strong anti-interference ability, which can effectively filter the low frequency noise in the process. Therefore, this paper analyzed the characteristics state of tool wear's acoustic emission signal. Metal in the cutting process will produce a wealth of acoustic emission signals, and these signals are easy to be affected by the comprehensive factors such as processing materials, cutting conditions and cutting parameters. A number of cutting experiments showed that the tool wear was uncertain under the same cutting conditions. Cloud model theory is a kind of qualitative concept to quantitative data uncertainty transformation model proposed by academician Li Deyi. The cloud theory has strong adaptability to the uncertainty problem. In view of the tool wear acoustic emission signal's non-stationarity and uncertainty, signal analysis and feature extraction method were put forward based on cloud theory. This paper aimed at the research topic of tool wear condition monitoring under different cutting condition, and used orthogonal test method to arrange a large number of cutting tests. On the basis of the acquisition of the acoustic emission signal, wavelet packet analysis method was applied to realize the signal filtering processing. A cloud of uncertainty model theory was introduced into the feature extraction of the different cutting tool wear stages. First of all, the different stages of the wear band distribution of acoustic emission signal range were obtained through the spectrum analysis, and served as the wavelet packet decomposition levels of qualitative reference; second, the Shannon entropy in the information entropy theory was applied to characterize the size of the noise in order to determine the best wavelet packet decomposition tree; finally, we used statistical analysis method to determine the best wavelet packet decomposition tree of the optimal branch, and after signal threshold processing, signal was reconstructed, and the denoising effect was verified through the ratio of signal to noise. In view of the tool wear condition monitoring and wear prediction research, feature extraction is a key technology. Therefore, this paper put forward the signal feature extraction method based on cloud theory. First of all, according to the statistical distribution characteristics of reconstructed signal, backward cloud algorithm was utilized to extract the cloud characteristic parameters of signal sensitive band, i.e. expected value, entropy and hyper entropy; the change rule of the 3 types of cloud characteristics parameters of cutting tool with the increase of wear was quantitatively analyzed in different cutting conditions; second, the effectiveness of 3 kinds of parameters characterizing tool wear acoustic emission signal during feature extraction was analyzed through a scatter diagram; finally, the effectiveness of the cloud model to represent knowledge was verified through data histogram and cloud image contrast. The research results show that the tool wear acoustic emission signal has obvious characteristics of cloud, and 3 cloud characteristic parameters and tool wear status have obvious corresponding relation, which can be used for characteristic parameters of tool wear condition monitoring and wear prediction. Cloud theory is applied in the field of tool wear monitoring, expanding the scope of the representation of knowledge. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]