Journal of Modern Mechanical Systems and Machining

Abstract

The rapid evolution of Computer Numerical Control (CNC) machining has demanded smarter and more efficient control systems. Conventional CNC systems, while reliable, often fail to optimally adjust to dynamic changes in cutting conditions, leading to reduced productivity and tool wear. Adaptive control systems driven by Artificial Intelligence (AI) have emerged as a solution, enabling real-time monitoring, parameter optimization, and fault prediction. This review paper explores AI-driven adaptive control (AI-AC) in CNC machining, highlighting its methodologies, benefits, challenges, and future potential. Key AI techniques such as neural networks, fuzzy logic, and reinforcement learning are examined for their role in enhancing machining efficiency and product quality. The paper also discusses integration challenges, implementation strategies, and comparative analyses of AI-based adaptive control systems versus conventional control methods.

Keywords: AI, CNC machining, adaptive control, neural networks, fuzzy logic, reinforcement learning, tool wear, process optimization