Defect detection of single point cutting tool using vibration signals and decision tree algorithm

ARTICLE INFO Tool wear and tool life are the principle areas are focus in any machining activity, the production rate, surface finish of the machined component and the service life of machine are directly related to the defects in the tool. Vibration signals and expert system like decision tree algorithm can be used to prevent the damage on cutting tools and work pieces when the defect in the tool arises. These studies have more relevant in manufacturing industries in order to match up with the competition. Based on the vibration signals and using expert system (decision tree algorithm), it is possible to find out the defects in the tool and different parameter which affects on the production rate. Decision tree algorithms are mostly used to study the structural health of the cutting tool. This method based on the analysis of defect detection in single point cutting tool using vibration signal which were obtained from the FFT and these tool vibration signals are used for obtaining various statistical features which indicates the various defects in the single point cutting tool. Condition monitoring is used for increasing machinery availability and performance, reducing consequential damage, increasing machine life, reducing spare parts inventories and reducing breakdown maintenance. Keywords— accelerometers, Decision tree algorithm, Defect detection, statistical features. Article History Received :18 th November 2015 Received in revised form :