Improving Tool-Life Stochastic Control Through a Tool-Life Model Based on Diffusion Theory

It is known that estimating the wear level at a future time instant and obtaining an updated evaluation of the tool-life density is essential to keeping machined parts at the desired quality level, reducing material waste, increasing machine availability, and guaranteeing the safety requirements. In this regard, the present paper aims at showing that the tool-life model that Braglia and Castellano [23] developed can be successfully adopted to probabilistically predict the future tool wear and to update the tool-life density. Thanks to the peculiarities of a stochastic diffusion process, the approach presented allows deriving the density of the wear level at a future time instant, considering the information on the present tool wear. This makes it therefore possible updating the tool-life density given the information on the current state. The method proposed is then experimentally validated, where its capability to achieve a better exploitation of the tool useful life is also shown. The approach presented is based on a direct wear measurement. However, final considerations give cues for its application under an indirect wear estimate.