Papers by Keyword: Continuous Chip

Abstract: This research presents the chip breaking detection system by monitoring the cutting temperature during the in-process turning. The effects of cutting parameters on the cutting temperature and the chip formation are investigated. The in-process monitoring of chip formation is developed to detect the continuous chip, the mixed chip and the broken chip by utilizing the ratio of the maximum variance of the dynamic cutting temperature to the average variance of the dynamic cutting temperature. The broken chip formation is required for the reliable turning operation. The new algorithm is proposed to obtain the broken chip by changing the cutting conditions during the cutting process referring to the cutting temperature. It has been proved by series of cutting experiments that the broken chip can be well identified by the proposed method.

Abstract: The aim of this research is to propose and compare the in-process detection systems of the cutting states of the continuous chip, the broken chip and the chatter for the carbon steel in CNC turning process by utilizing the sensor fusion, which are the force sensor, the sound sensor, the accelerometer sensor and the acoustic emission sensor. The new six parameters proposed for the inputs of the neural network systems, which are the enegy spectral densities of three dynamic cutting forces, sound signal, accelation signal, and the standard deviation of acoustic emission signal. All signals of parameters have been integrated via the different neural network systems by using the pattern recognition and the percertron technique to detect the cutting states, which are. Among the cutting states of chip formation and chatter, the broken chip is required for the reliable and stable cutting system. The experimentally obtained results showed that the in-process detection system using the neural network with the pattern recognition technique can be effectively used to detect the cutting states with the higher accuracy and reliability more than the one with the perceptron technique.

Abstract: In order to realize the intelligent machines, an in-process monitoring system is developed to detect the continuous chip and the broken chip regardless of the cutting conditions on CNC turning by utilizing the power spectrum density, PSD of dynamic cutting force and the variance of the dynamic cutting temperature, which are measured during the cutting by employing the dynamometer and the infrared pyrometer. The broken chip formation is required for the reliable turning operation. The preliminary experiments suggested that there are basically two patterns of PSDs of chip forms. One is the case of relatively high PSD of the dynamic cutting force at low frequency range, which corresponds to the continuous chip formation. The other is the case of relatively large PSD of the dynamic cutting force observed in a frequency range corresponding to the chip breaking frequency when the broken chip are formed. The variances of the cutting temperature are also significantly different between the broken chip and the continuous chip. Hence, the method has been developed by using the PSD of dynamic cutting force and the variance of cutting temperature to determine the proper threshold values for classification of the broken chip and the continuous chip during the cutting. The new algorithm is proposed to obtain the broken chip by changing the cutting conditions during the cutting process. It has been proved by series of cutting experiments that the broken chip can be well identified by the proposed method even though the cutting conditions are changed.

Abstract: As the intelligent machine and manufacturing system plays an important role in the near future, the monitoring system in turning process is required to improve the productivity during the cutting process. Hence, the aim of this research is to propose and develop the in-process monitoring system of the tool wear and the cutting states of chip and chatter for the carbon steel in CNC turning process by utilizing the sensor fusion which are the force sensor, the sound sensor, the accelerometer sensor and the acoustic emission sensor. Their signals have been integrated via the neural network with the back propagation and the pattern recognition technique to monitor the tool wear and detect the cutting states which are the continuous chip, the broken chip and the chatter occurred. The experimentally obtained results showed that the in-process monitoring system proposed and developed in this research can be effectively used to estimate the tool wear level and identify the chip breaking and the chatter with the higher accuracy and reliability.