Technology Of Classification And Forecast Of Tools Wear Based On Multi-feature Analysis And Fusion Of AE

With the development of automation of manufacturing equipment and integration of manufacturing mode, real-time tool condition monitoring has become key technology in achieving automatic machining process and is an important problem not solved currently. The research on tools condition monitoring in machining process is of great importance and urgent to the realization of automatic machining process, improvement of product quality, and high productivity.To classify tools wearing and forecast wearing capacity under alternating cutting conditions, a multi-feature fusion method based on least squares support vector machines (LS-SVM) was established through the analysis of large quantity of experimental data. Modern digital signal processing (DSP) methods such as empirical mode decomposition, high-order spectral analysis and wavelet transform were used in the characteristics extraction of tools wearing. The main accomplishments are summarized as follows:1,In the past, traditional signal processing methods are mostly used in characteristics extraction in monitoring tools wearing. Methods such as statistical analysis in time domain, Fourier transform and PSD estimation cannot meet the signal processing requirements because signals from tools wearing are non-stationary, non-linear and non-Gaussian. In this paper, Modern signal processing methods such as empirical mode decomposition, high-order spectral analysis and wavelet transform are introduced tool wear signal extraction.First, the method of characteristics extraction of tools wearing based on the combination of Empirical mode decomposition (EMD) and autoregressive (AR) model is proposed. EMD can decompose non-stationary emission signals into finite intrinsic mode function (IMF) components. The method of IMF selection based on correlation coefficient method was proposed with the aim of addressing the issue that EMD of acoustic emission signals at different wearing stages produce different numbers of IMF. AR model coefficient contains important information of system and is most sensitive to status changes, so AR model was established for each IMF component after constructing feature vectors via extracting model coefficient. The order of AR model directly influences the accuracy of model and system identification. The order determination of AR model was addressed using sample acoustic emission signals of tools wearing. Forth-order AR model proved to be accurate by prediction error analysis. It is indirectly proved that the AR model coefficients as feature vectors characterize the effectiveness of the tool wear.Second, the theory of bispectrum analysis was successfully applied to the characteristics extraction of tools wearing. As a result, a method of feature extraction based on bispectrum singular value decomposition was proposed. After the removal of mean values and the normalization process of experimental data from different cutting conditions and different wearing stages, an eigenvector matrix was constructed on the basis of the bispectrum analysis of experimental data. The eigenvector was subsequently constructed by extracting singular spectrum through singular value decomposition of eigenvector matrix. By analyzing sample acoustic emission signals of tools wearing, this method proved to be effective in reducing the influence of cutting conditions and suitable for feature extraction of tools wearing under alternating cutting conditions.Next, The energy characteristics extraction of wavelet package and statistical feature of time domain, which is established on the basis of the best bases of wavelet package, was proposed. Determination of decomposing levels and selection of best bases of wavelet package were investigated using sample acoustic emission signals. High dimensional time domain statistical features was evaluated on the distance ratio of within/between class. As a result, the dimension of time domain was reduced from 72 to 13. This improved the performance of feature classification and avoids the Dimensional disaster.2,A characteristics extraction method of tools wearing based on multi-feature fusion was proposed. In the traditional monitoring system of tools wearing, the sensor fusion based monitoring method is used by many researchers to improve the accuracy of monitoring. But this traditional method has some limitations in its practical applications. First, it largely increases the cost of the monitoring system. Second, installing of monitoring system may interfere the operator, even impede the performance of machine tools. In view of this, multiple feature joint base on modern signal processing methods was proposed. The characteristics of the same signal in different domains was extracted by modern signal processing technology, then the joint multi-feature vector was contructed. By using the Kernel principal component analysis method, the joint multi-feature vector was fused. Select the principal components whose Cumulative contribution rate is greater than 85% and generate fusion feature vector corresponding to the tool wear, which can effectively exclude features that is redundant or has little relevance to tool wear. Divergence diagram of the fusion feature after descending dimension shows that: the fusion features retained have better character of clustering.3,Introduced the least squares support vector machines to tool wear classification and wear prediction. To overcome shortcomings such as large quantities of samples required in artificial neural network training, low convergence rate of learning algorithm and trapped in local minimum in training, least squares support vector machines was applied to classifying and forecasting tools wearing. The analysis of sample data proved that the identification rate of fusion characteristic is higher than that of single one, and the model of classification and forecasting based on least squares support vector machines is superior to the one based on artificial neural network. Using least squares support vector machine regression algorithm, by constructing a parallel dual regression support vector machine, tool wear prediction 10s ahead is effectively realized.4,A monitoring system of tools wearing based on digital signal processor was developed. The features of this system are: fast computing speed to meet the requirement of real-time monitoring; high flexibility because all algorithms for classification and forecasting are realized using software only; and universal interface for sensor signals so that sensors can be replaced at any time as required by real-time signals. This system can be used as a digital collector even the signal characteristics and algorithms are unknown because the high speed USB interface can transfer data to computer for prompt processing. After being analyzed and reprogrammed for classification and forecasting by the users, the data are fed back to the system. As a result, monitoring of cutting conditions by different processing methods can be realized. Theoretically, this monitoring system can be applied to any processing method.