A probabilistic-based study on fused direct and indirect methods for tracking tool flank wear of Rene-108, nickel-based alloy

Nickel-based alloys are a class of hard-to-machine materials that exhibit a unique combination of high strength at high temperature. While they are widely used in industry, the high tool wear rate of these materials makes machining them a challenging task. Furthermore, machining tolerances, residual stresses, and tool runout impose uncertainty in tracking tool wear. In this work, the current view on tracking progressive tool wear is shifted from deterministic domains into the stochastic domain to study the probability distribution of the tool wear during the process. To do so, Bayesian-based estimation was used for accurate model inference and the result was fed into the Kalman filter for tracking tool wear in end-milling Rene-108 Ni-based alloy. To improve the accuracy, a direct laser measuring system for capturing tool length change was fused with the indirect power measurement. The results show a significant improvement in accuracy over the indirect method, with less than 8 mu m root mean square error. This measuring strategy improved the accuracy, while preserving the automated platform for monitoring the tool's health.