Reinforcement Learning for Dynamic Process Optimization in Manufacturing

Abstract

Manufacturing process optimization has traditionally relied on staticparameter settings derived from design of experiments (DOE) or model-based control approaches that assume stationary process conditions. However, real-world manufacturing processes exhibit dynamicbehavior—tool wear progression, thermal drift, material batch variability, and environmental fluctuations—that cause optimal operating parameters to shift continuously during production. Reinforcement learning (RL) offers a fundamentally different optimization paradigm in which an intelligent agent learns optimal control policies through direct interaction with the manufacturing process, continuously adapting to changing conditions without requiring explicit process models. This paper presents the development and industrial validation of a deep reinforcement learning (DRL) framework for dynamic optimization of CNC turning process parameters. The proposed system employs a Soft Actor-Critic (SAC) algorithm that continuously adjusts cutting speed, feed rate, and depth of cut in response to real-time sensor feedback from cutting force,vibration, and acoustic emission sensors to simultaneously minimize surfaceroughness, maximize tool life, and maintain dimensional accuracy withintolerance. The system was validated on an industrial CNC lathe machiningAISI 4140 alloy steel over a 3-week production period comprising 480machining operations. The RL-optimized process achieved 18.4%improvement in surface roughness (Ra 0.68 µm vs. 0.83 µm baseline), 26.8% extension of tool life (142 min vs. 112 min), and 100% dimensionalconformance, while the static DOE-optimized baseline achieved 94.2%conformance. The findings demonstrate that DRL-based adaptive processcontrol can achieve superior manufacturing outcomes by continuouslylearning and adapting to dynamic process conditions [1], [2].  KEYWORDS: Reinforcement Learning, Process Optimization, CNCMachining, Deep Learning, Adaptive Control, Soft Actor-Critic, Tool Wear,Surface Roughness, Smart Manufacturing, Industry 4.0