Methods Of Monitor And Recognition For Chatter In Cutting Process

Cutting is a machining way, by which superabundant material layer is cut by cutting tools from blank or work piece, in order to get specific figure, size and surface quality. Chatter in cutting process is one of the main causes which can affect the quality of work piece. It is difficult to accurately measure and identify chatter because of its nonlinear, time variation and uncertainty. For many years, lots of scholars from various countries always tried to study chatter and have achieved some important results. But there are still some problems that need to be solved. In this paper, the theories and methods of Markov, Support Vector Machine and Kernel Principal Component Analysis was synthesized, and was applied in diagnostic analysis of startup process and tool wear which are inducing factors for chatter, and the study about methods of monitor and identification of chatter in cutting process was performed. The main works in the research are as follows:(1) Through analysis of fault feature of startup process in cutting, based on Markov's theory,Continue Hidden Markov Model with mixture probability Densities(CDHMM) was established in the research in order to identify fault condition during startup process. When it was used in identifying running state of startup process the overflow of Markov's model was settled. Characteristic information including workpiece loosing, unbalance, asymmetry and normally starting was extracted based on Gaussian density function,, then the CDHMM did identify and diagnose fault according to these information.The new model was better than the traditional diagnostic method for it overcame the traditional model's defect of losing information in feature information abstraction.The new method was simple, good in recognition rate, and suitable for diagnosis of startup fault about rotating machinery. The experimental results showed that the new model had better recognition effect than Hidden Markov model.(2) After analysis the effect of tool wear to chatter, a tool wear diagnosis model was established based on discrete hidden Markov model(DHMM). Dynamic cutting force signal and tool holder vibration signal was dealt by fast fourier transformation, and characteristic parameter was extracted and was normalized, then the parameter was presorted and coded by self-organizing feature maps. The coding value was introduced into the discrete hidden Markov models as observation sequence for machine learning, which could recognize condition of tool wear and feeding quantity was controlled according to the recognition results. The model overcame defect of the traditional identification means including large calculation and complex algorithm. The new model had higher recognition speed and better real-time character.The experimental results showed that the new method had better recognition results than the Hidden Markov Model which could establish basement for recognizing chatter correctly.(3) According to the nonlinearity, uncertainty and variability of signal of vibration and cutting force, a diagnostic model of fault was built in the research based on Kernel Principal Component Analysis and Support Vector Machine (KPCA-SVM) through extracting a large sample of cutting. This model extracted linear principal component information from nonlinear chatter value through KPCA, the linear principal component which could reflect character of chatter could be identified according to the contribution rate of principal component information. Linear principal component was classified by one-to-many mode and classification results could be used for judging chatter trend which provided data foundation for controlling duty. This method could fully describe development of chatter which is difficult for the traditional method. Experimental results showed that KPCA-SVM was a new effective method for recognizing chatter trend and better than PCA-SVM (Principal Component Analysis and Support Vector Machine) for large samples which could describe cutting process.(4) A diagnostic model was established based on Support Vector Machine and Hidden Markov Model(SVM-HMM) to recognize degree of chatter for small samples when chatter occurring in cutting process. This model got optimum rate of small sample in SVM, and the output results from SVM was transformed into Sigmoid probability, which then was transported into HMM model, where vibration signals and cutting force signals was effectively studied and recognized through HMM's ideal class classification ability. In the research, the results showed that this method had better ability in recognition of cutting chatter and could get better recognition effect than SVM and HMM for small samples. This method overcame the disadvantage of recognizing non-chatter information as chatter and it was a new method for diagnosis of chatter.