Abstract

ABSTRACT

Tool condition monitoring (TCM) is critical for enhancing machining performance, tool life, and product quality in advanced manufacturing. This study proposes a simulation-driven framework for predictive tool wear monitoring using vibration signal analysis and Finite Element Analysis (FEA)-based stress and modal evaluation. A Taguchi L27 design of experiments was applied to investigate the effects of cutting speed, tool tip radius, and depth of cut on tool vibration, stress, and deformation. The cutting tool was modeled using CAD and analyzed under various loading and boundary conditions using FEA simulations. Results revealed that higher cutting speeds and depths of cut significantly increased vibration frequencies and von Mises stress, indicating a higher likelihood of wear and instability. The modal analysis further showed sensitivity of natural frequencies to geometric and process changes, suggesting their potential use as wear indicators.