A novel approach for in-situ characterization and probabilistic prediction of cutting tool fatigue in machining of Ti-6Al-4V

Abstract

The wear behavior of cutting tools is highly complex due to combined thermal, mechanical, and chemical loads. As a result, most current tool-wear prediction methodologies are either empirical or highly oversimplified analytical or numerical models of stable abrasive and diffusive wear mechanisms. To predict the complex physics of catastrophic tool edge chipping, which in practice bounds feasible process parameters, this manuscript presents a novel approach for in-situ characterization of tool edge fatigue loads and probabilistic prediction of the likelihood of time to fracture. The results of the analysis suggest encouraging possibilities for more physics-informed and data-driven process design.