Adaptive resampling-based particle filtering for tool life prediction

Abstract

Trending analysis has been widely investigated for prediction of tool wear, which impacts not only tool life but also the quality of machined products. This paper presents a Bayesian approach to predicting the flank wear rate, linking vibration data measured during machining with the state of tool wear. Variations in the measured data are aggregated based on the Kullback–Leibler divergence, which provides a measure for the distance between the current and initial probability distributions of measurement, when no wear is present. Subsequently, state space estimation of the tool wear is realized by particle filtering (PF), a non-linear and non-Gaussian system estimation technique. To overcome the sample impoverishment problem in sequential importance resampling (SIR), a new resampling scheme is proposed, which has shown to more reliably quantify the confidence interval and improve the prediction accuracy of the remaining useful life (RUL) prediction of the tool as compared to standard SIR method. The developed method is experimentally evaluated using a set of benchmark data measured from a high speed CNC machine that performed milling operations. Good results are confirmed by the comparison between the predicted tool wear state and off-line tool wear measurement.