Advanced Monitoring of Tool Wear and Cutting States in CNC Turning Process by Utilizing Sensor Fusion

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.