Intelligent monitoring and identification of cutting states of chips and chatter on CNC turning machine

Abstract To realize an intelligent machine tool, which can autonomously determine the cutting states and can change them automatically as required due to changes in the environmental conditions, a method has been developed to monitor and identify the states of cutting for CNC turning based on a pattern recognition technique. The method proposed introduces three parameters to classify the cutting states of continuous chip formation, broken chip formation, and chatter. Among the states of cutting, the broken chip formation is required for the stable and reliable machining process. The three parameters are calculated and obtained by taking the ratio of the average variances of the dynamic components of three cutting forces. The algorithm was developed to calculate the values of three parameters during the process to obtain the reference feature spaces and determine the proper threshold values for classification of the cutting states. A tool dynamometer is developed, and implemented to the CNC turning machine to monitor the turning process. It is proved by a series of cutting experiments that the states of cutting are well identified by the method developed and proposed regardless of the cutting conditions. The algorithm is proposed to obtain the broken chips by changing the cutting conditions during the process.