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). (c) 2005 Elsevier B.V. All rights reserved.