Development for a Prediction System of Chatter Vibration by using Fuzzy Neural Network Model

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This research is concerned with Chatter prediction system by using a fuzzy neural network model. The chatter vibration occurring in mechanical machining gives rise to poor surface finish and dimensional accuracy in machined part, reduction of tool life, and even damages machine tools. Various kinds of researches concerning its prediction and avoidance have been carried out over the last several decades. First, this study is to develop an expert system for predicting chatter vibrations in high-speed end milling using wavelet transform and a fuzzy neural network model. Second, wavelet packet transform is used for the fuzzy neural network model. By using wavelet packets, flexibility for selection of the number of neurons in input layer extend rather than the usual wavelet transform. Additionally, the hidden layer is divided into two blocks, the first block is related to the condition part and the second one consists of the cutting sound part. The convergence of learning and the accuracy of decision are investigated in this paper. The proposed method is applied to a jig grinding machine, and the results demonstrate the effectiveness of the chatter prediction procedure. But, it is difficult to measure cutting sound in operation. The measured sounds include environmental noise. The research has been carried out to separate the cutting sounds from the measured data. As the problem is regarded as a BSS problem, the separation of cutting sounds from the measured data is performed by ICA algorithm and the subspace identification method. The procedure needs further improvement in the future.