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. These include the optimization of parameters such as reducing the feed and speed, use of proper coating such as TiN and Al2O3, lubrication/cooling, and proper material for the cutting tool like carbide and ceramic materials. The application of chip breakers and high machine rigidity can minimize wear by lowering heat and friction, which are the major causes of wear. Reduction in wear will ensure a better surface finish, enhanced tool life, and economic efficiency of the machining process. The main objective of this research paper is to conduct an extensive study on wear of cutting tools in machining operations. As a result, the study discusses several advanced methods of tool wear detection in cutting tools, including sensor-based methods, machine vision, and AI/ML-assisted predictive maintenance. Additionally, a critical assessment in tool wear minimization is conducted to apply new material to the cutting tool, the coating process, cutting parameter and path optimization, cooling and lubrication systems such as minimum amount lubrication and cryogenic cooling. Moreover, various challenges with intelligent and autonomous manufacturing systems that arise in tool wear prediction with regard to availability of data and reliability of prediction models are discussed in the study. Finally, potential future research directions are provided, with an emphasis on the importance of using digital twin technologies and sustainable manufacturing approaches in tool wear management.