Turning tool wear monitoring based on fuzzy cluster analysis

Abstract

There are several stages of tool wear in turning process. We collect of the force signals and vibration signals at each stage. Using wavelet filtering and power spectrum methods, typical parameters changes are detected. We extract the signal feature for fuzzy clustering. Experimental results show that the tool wear monitoring is achieved in turning by using this pattern recognition method. © 2010 Springer-Verlag Berlin Heidelberg.