Cutting Tool Wear Minimization in Machining Operations: A Review

Cutting tool wear significantly influences machining performance, surface quality, and manufacturing cost. Proper minimization of cutting tool wear will result in enhanced life of the cutting tool, surface integrity, precision, and sustainability of the machining process. There are various methods for minimizing cutting tool wear in machining operations, including the optimization of parameters such as reducing the feed and speed, use of proper coating such as TiN and Al2O3, lubrication/cooling, and selecting suitable materials for the cutting tool like carbide and ceramic. The application of chip breakers and high machine rigidity contributes to minimizing wear by lowering heat and friction, which are major causes of wear. This research paper's main objective is to conduct an extensive study on the wear of cutting tools in machining operations, and it discusses advanced methods of tool wear detection, including sensor-based methods, machine vision, and AI/ML-assisted predictive maintenance. A critical assessment of tool wear minimization strategies is provided alongside discussions of challenges in tool wear prediction in intelligent manufacturing systems, particularly regarding data availability and prediction model reliability. The study concludes with potential future research directions emphasizing digital twin technologies and sustainable manufacturing in tool wear management.