Tool wear forecast using Singular Value Decomposition for dominant feature identification

Identification and prediction of lifetime of industrial cutting tools using minimal sensors is crucial to reduce production costs and down-time in engineering systems. In this paper, we provide a formal decision software tool to extract the dominant features enabling tool wear prediction. This decision tool is based on a formal mathematical approach that selects dominant features using the singular value decomposition (SVD) of real-time measurements from the sensors of an industrial cutting tool. It is shown that the proposed method of dominant feature selection is optimal in the sense that it minimizes the least-squares estimation error. The identified dominant features are used with the recursive least squares (RLS) algorithm to identify parameters in forecasting the time series of cutting tool wear on an industrial high speed milling machine.