Design and performance analysis of wear-resistant micro-groove cutting tool: Numerical simulation and experimental study

The rake face serves as a critical stress concentration zone where mechanical and thermal loads predominantly distribute, making its geometric optimization paramount for cutting performance enhancement. This investigation establishes a novel computational framework integrating finite element modeling (FEM), machine learning (ML)-based predictive analysis, and multi-objective genetic algorithm (GA) optimization for micro-groove tool design. Four distinct groove morphologies—triangular (TIG), arcuate (ARG), straight-arc (SAG), and arc-straight (ASG)—were parametrically modeled and comparatively analyzed through thermomechanical FEM simulations. ML algorithms were employed to develop predictive surrogate models, subsequently driving GA optimization of critical geometric parameters. Experimental validation reveals the ASG configuration achieves superior performance. Tools with ASG structure reduce significantly cutting force and temperature, and improve chip curling. The optimized micro-groove structure not only shortens the tool-chip contact length but also reduces the effective rake angle, enhancing the storage capacity for cutting fluids. Consequently, this improvement facilitates better cooling and lubrication. These synergistic effects yield a 37.5 % extension in tool life, attributable to reduced abrasive/adhesive wear mechanisms. The proposed methodology demonstrates significant potential for enhancing the surface structure of intelligent cutting tools and improving their wear resistance.