Research On Drill Wear Monitoring Technology Of The Multi-Feature Fusion Based On HMM

The development of manufacturing equipment automation and manufacturing mode integration makes tools become a weak link in manufacturing system. Accordingly the research and development of the tool condition monitoring technology in machining process is of great significance in realizing automation of machining process and upgrading the manufacturing level. With the drilling process as the research objective, the drill wear monitoring experimental system with the drilling force as the monitoring signal is established in this thesis to carry out the systematically theoretical and experimental researches on the feature extraction of drilling force signal as well as the technology of the multi-feature fusion drill wear monitoring based on HMM.Starting from the angles of the time and frequency domain respectively, the relationships between the drilling force signal as well as its feature value and drill wear in drilling process are investigated in this thesis, and it has been found that there is a rather poor correlation between the statistic feature and drill wears in the time domain while in the frequency domain, the power spectrums of drilling force signal showed good correlation with drill wear, and the statistic features appear to have the changing trend basically agreeable with the drill wear laws.The multi-resolution performances of wavelet transform as well as the basis principle of Mallat algorithm are discussed. The Daubechies5 orthogonal wavelet is adopted to carry out the multi-resolution analysis to the drilling force signals in the time and frequency domain. The changing laws of the wavelet decomposed signals and its statistic features are also studied so that the statistical features that are correspond to drill wears are obtained from the low frequency wavelet decomposed coefficient and the reconstructed power spectrum envelope of the drilling force signals, and this lay a solid foundation for the realization of drill wear condition monitoring.The fractal theory has been successfully introduced into the field of drill conditionmonitoring. Also the grid establishment in the process of calculation the box dimension of the discrete time series signals as well as the method for determining the delay time and implanted dimension in time series reconstruction are studied. The algorithms for calculating the three kinds of fractal dimension of the discrete time series signals are given in this thesis. The changing laws of fractal dimension of the signals in the time domain and the wavelet reconstruction signals in each frequency band are studied in drilling process. The results show that with the increase of drill wears, the box dimension and the information dimension as well as the correlation dimension of drilling force signals appear obviously to have the downtrends. Accordingly, this characteristic can be effectively used to realize the monitoring of drill wears.Aiming at the characteristics of the highest overlap of the feature modes in each drill wear condition, the hidden Markov model is put forward to resolve the drill wear monitoring. The systematic studies on the principle and realization method of HMM used in drill wear monitoring are carried out. The implementing procedures for model training and identification based on forward and backward algorithm and Baum-Welch algorithm are completed. The vector quantization system based on SOM network has been formed to resolve the problem of the fusion and coding of the drilling force feature vectors.Two kinds of methods for drill wear monitoring based on HMM are put forward. The effective features obtained from the time and frequency domain, wavelet transform and fractal are used to carry out drill wear monitoring of multi-feature fusion. The experimental results indicate that probabilities of unknown observation series can reflect the statistic similarities of observation series in different drill wear status and track the development trend of drill wear. Moreover using the multi-model method has successfully realize the recognition of three typical drill wea...