Pimenov Danil Yu, Ribeiro da Silva Leonardo Rosa, Kuntoğlu Mustafa, Abrão Bruno Souza, Dos Santos Paes Luiz Eduardo, Linul Emanoil
Department of Automated Mechanical Engineering, South Ural State University, Lenin Prosp. 76, Chelyabinsk 454080 Russia.
Faculty of Mechanical Engineering, Federal University of Uberlândia, Av. João Naves de Ávila, 2121, 38400-902, Uberlândia, MG, Brazil.
J Adv Res. 2025 Jan 10. doi: 10.1016/j.jare.2025.01.013.
Today, in a wide variety of industries, grinding operations are an extremely important finishing process for obtaining precise dimensions and meeting strict requirements for roughness and shape accuracy. However, the constant wear of abrasive tools during grinding negatively affects the dimensional and surface conditions of the workpiece. Therefore, effective monitoring of the wear process during grinding operations helps to predict tool life, plan maintenance and ensure consistent product quality.
The objective of this review is to examine current tool condition monitoring techniques, both direct and indirect, in various sensor systems and their application in both traditional and AI-driven grinding processes. By examining these techniques, the review provides insight into how different monitoring techniques can improve process efficiency, reduce downtime, and improve finished product quality, as well as the application of intelligent and adaptive processes to traditional grinding operations.
The review discusses the critical role of sensor systems in monitoring tool condition, including technologies such as imaging, vibration analysis, acoustic emission, and force measurement. These systems are vital for detecting wear and predicting failures, allowing for timely interventions and preventing unplanned downtimes. The integration of artificial intelligence into these monitoring systems greatly enhances their capabilities, as they enable more proactive strategies and adapt to changing conditions during the grinding process.
