DEVELOPMENT OF ADAPTIVE CONTROL SYSTEMS FOR TOOL WEAR REDUCTION

Tool wear significantly affects machining efficiency, leading to increased costs, reduced precision, and frequent maintenance. The integration of advanced monitoring techniques and predictive maintenance strategies has revolutionized tool wear management, enabling real-time analysis and proactive interventions. This paper explores various methodologies, including machine learning models, sensor fusion, and adaptive control systems, to optimize tool life and machining efficiency. The challenges associated with data-driven approaches, implementation in diverse industrial environments, and the scalability of adaptive control systems are also discussed. The study highlights the need for continued research in integrating artificial intelligence (AI) and real-time monitoring to enhance predictive capabilities and improve machining processes.