Comparison of in-Process Cutting State Detection in CNC Turning Using Different Neural Network Systems
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.
Dongye Sun, Wen-Pei Sung and Ran Chen
S. Tangjitsitcharoen and A. Senjuntichai, "Comparison of in-Process Cutting State Detection in CNC Turning Using Different Neural Network Systems", Applied Mechanics and Materials, Vols. 121-126, pp. 1942-1946, 2012