Tool wear detection in turning operations using singular spectrum analysis

Singular spectrum analysis (SSA) is a new non-parametric technique of time series analysis, based on principles of multivariate statistics, that decomposes a given time series into a set of independent additive time series. Fundamentally, the method projects the original time series onto a vector basis obtained from the series itself, following the procedure of principal component analysis. In the present work, SSA is applied to the analysis of the vibration signals acquired in a turning process in order to extract information correlated with the state of the tool. That information has been presented to a neural network for determination of tool flank wear. The results showed that SSA is well-suited to the task of signal processing. Thus, it can be concluded that SSA is quite encouraging for future applications in the area of tool condition monitoring (TCM).