Reinforcement learning for precision metal cutting

Abstract

This paper examines the application of reinforcement learning (RL) techniques in precision metal cutting, with a core emphasis on laser cutting and brief extensions to CNC milling, EDM, and abrasive waterjet cutting. It describes closed-loop architectures that combine multi-sensor feedback with neural network-based RL agents capable of real-time decision-making. The study explores the suitability of algorithms such as Q-learning, Deep Deterministic Policy Gradient (DDPG), and Proximal Policy Optimization (PPO) for high-frequency, continuous control tasks. Drawing on a patented AI-controlled laser cutting module, the paper demonstrates how RL improves adaptability to material variability, enhances cut quality, and minimizes operator intervention. Case studies and industry data support the performance benefits of RL-based systems over conventional static control. The article concludes with a discussion of implementation strategies, safety considerations, and the broader role of RL in future smart manufacturing environments.