An inexpensive system for classifying tool wear states using pattern recognition

The purpose of this research work is to develop an inexpensive model tool wear sensing system using pattern recognition. Accordingly, the combined output of radial force, feed force and acoustic emission (r.m.s. value) is utilized to model the tool flank wear in a turning operation. The tool wear sensing system consists of two phases: training and classification. The training phase is done off-line and is used to determine the weight coefficients for the linear decision functions using the prototype patterns from the cutting tests. The classification phase is in real time. In the first stage of the classification phase, the minimum distance classifier selects a prototype (conditions already trained) cutting test that is closest to the cutting test to be performed. The linear decision functions of the prototype test selected are used for classifying the incoming signal of the actual cutting test into one of three wear classes.

The success rate of training for various tests varied between 39.57% and 100%. The success rate of classifying signals from actual tests was also encouraging, demonstrating that the proposed methodology can be successfully applied to predict the status of the cutting tool on-line using low budget equipment.