Journal of Water Resource Engineering and Pollution Studies ISSN: 3107-8419 (Online)

Authors:  Dr. Kavita Sharma, Rajdeep Verma

Abstract:  Tool path optimization in CNC machining plays a crucial role in enhancing productivity, precision, and energy efficiency in modern manufacturing environments. Traditional methods of tool path generation depend on static algorithms and operator knowledge, leading to suboptimal performance and high production costs. The integration of Artificial Intelligence (AI) into CNC tool path planning has emerged as a transformative approach, offering real-time decision making, self-learning capabilities, and dynamic path correction. This paper explores the automation of tool path optimization using AI algorithms such as Genetic Algorithms (GA), Artificial Neural Networks (ANN), and Reinforcement Learning (RL). The study also evaluates the comparative performance of AI based techniques against traditional methods, highlighting key metrics such as machining time, surface quality, and tool wear. Experimental findings demonstrate that AI-driven systems can adapt to real-time feedback, reduce idle time, and achieve superior geometric accuracy in complex machining tasks. The research aims to bridge the gap between intelligent manufacturing systems and industrial automation, paving the way for fully autonomous CNC operations in Industry 4.0 environments.

Keywords: CNC Machining, Tool Path Optimization, Artificial Intelligence, Genetic Algorithm, Reinforcement Learning, Automation, Smart Manufacturing.