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 descr...
| Autores: | , , , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión enviada para evaluación y publicación |
| Fecha de publicación: | 2026 |
| País: | España |
| Institución: | Basque Center for Applied Mathematics (BCAM) |
| Repositorio: | BIRD. BCAM's Institutional Repository Data |
| OAI Identifier: | oai:bird.bcamath.org:20.500.11824/2149 |
| Acceso en línea: | http://hdl.handle.net/20.500.11824/2149 |
| Access Level: | acceso abierto |
| Palabra clave: | drilling Bayesian inference tool wear State-space model degradation process Stan |
| Sumario: | 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. |
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