Chatter often poses limiting factors on the achievable productivity and is very harmful to machining processes. In order to avoid effectively the harm of cutting chatter,a method of cutting state monitoring based on feed motor current signal is proposed for chatter identification before it has been fully developed. A new data analysis technique,the empirical mode decomposition(EMD),is used to decompose motor current signal into many intrinsic mode functions(IMF) . Some IMF's energy and kurtosis regularly change during the development of the chatter. These IMFs can reflect subtle mutations in current signal. Therefore,the energy index and kurtosis index are used for chatter detection based on those IMFs. Acceleration signal of tool as reference is used to compare with the results from current signal. A support vector machine(SVM) is designed for pattern classification based on the feature vector constituted by energy index and kurtosis index. The intelligent chatter detection system composed of the feature extraction and the SVM has an accuracy rate of above 95% for the identification of cutting state after being trained by experimental data. The results show that it is feasible to monitor and predict the emergence of chatter behavior in machining by using motor current signal.
LIU HongQilCHEN QmgHaLI BinMAO XinYongMAO KuanMinPENG FangYu
The vast majority of tool condition monitoring systems use the motor current instead of the cutting force as the predictor signal. The measured motor current signal is time-dependant and instable. It is difficult to detect the cutting force token signal from such motor current signal. This paper presents a method that uses the wavelet transforms to reconstruct the cutting force token signal from the current signal based on the time frequency analysis of the cutting force signal. The result of the cutting force measurement experiment shows that the proposed reconstruct method could be used to analyze the spindle current and monitor the time-varying cutting force.
