Tool condition monitoring in turning using fuzzy set theory

This paper presents a study on tool condition monitoring in turning using the fuzzy set theory. The tool conditions considered include tool breakage, several states of tool wear, and chatter. Force, vibration, and power sensors are used in this study to monitor the three components of the cutting force, i.e. acceleration of the tool holder in two perpendicular directions, and the spindle motor current respectively. A total of 11 monitoring indices (signature features) are selected to describe the signature characteristics of various tool conditions. A linear fuzzy equation is proposed to describe the relationship between the tool conditions and the monitoring indices. The proposed methodology is verified experimentally using a total of 396 cutting tests performed at 52 different cutting conditions. The proposed methodology is also compared with that of several classification schemes, including the K-mean and the Fisher's pattern recognition methods, the nearest neighbor method and the fuzzy C-mean method. The results indicate an overall 90% reliability of the proposed methodology for detecting tool conditions regardless of the variation in cutting conditions.