A Bayesian state-space model for tool wear estimation in drilling of Inconel 718

We study the evolution of tool wear during drilling of plates of Inconel~718 under different cutting speeds. As observable variable we use the spindle motor current, so no additional sensor system is required for monitoring. We propose two Bayesian state-space models with nonlinear dynamics to describe the system, where the latent state (tool wear) is modelled using a modified Gamma process. In our analysis, we also examine the variability from tool to tool --- which is usually neglected --- through a hierarchical model. We apply Bayesian inference to support our conclusions, and we also show a method to deal with missing information due to the difficulty measuring tool wear.