Identitication of Chip Form in Turning Process

Abstract

The machining process can be considered as a planned interaction of workpiece, tool, and machine tool. In an unmanned situation, the results of this interaction are to be continuously monitored so that any changes in the machining environment can be sensed in order to take corrective action. In order to design the process monitoring system for unmanned manufacturing, the identification of chip form is proposed. The system proposes the method of using acoustic emission signal analysis to identify the chip form during cutting. The acoustic emission signals are analyzed using the standard deviation, the skew, and the kurtosis of the beta distribution and the counted frequency at the acoustic emission mode. The fuzzy set theory and the neural network are applied to identify the chip form. In fact, the percentage of correct recognition of the identification of thip form using both the methods is always higher than 90% in experiments.