Flank wear detection of cutting tool inserts in turning operation: application of nonlinear time series analysis

It has been established that turning process on a lathe exhibits low dimensional chaos. This study reports the results of nonlinear time series analysis applied to sensor signals captured real time. The purpose of this chaos analysis is to differentiate three levels of flank wears on cutting tool inserts—fresh, partially worn and fully worn—utilizing the single value index extracted from the reconstructed chaotic attractor; the correlation dimension. The analysis reveals distinguishable dynamics of cutting characterized by different values for the dimension of the attractor when different quality tool inserts are used. This dependence can be effectively utilized as one of the indicators in tool condition monitoring in a lathe. This paper presents the experimental results and shows that tool vibration signals can transmit tool wear conditions reliably.