Digital Twin-based Deep Reinforcement Learning Framework to real-time Chatter Suppression and energy optimization in micro-turning to Industry 5.0

This paper introduces a Digital Twin-based Deep Reinforcement Learning (DRL) model that can be used to suppress chatter and optimize energy consumption in micro-turning within the humanistic paradigm of Industry 5.0. An accurate precision lathe machine equipped with vibration, spindle-current and temperature sensors and acoustic sensors feeds high-frequency data to a constantly changing digital replica that incorporates regenerative cutting-force dynamics, feature-extracting wavelets and Kalman-state estimation. The twin predicts immediate tool movement, chatter activity, torque variability, and thermal loading, allowing a Proximal Policy Optimization (PPO) agent to modify spindle speed and feed rate by using a predictive, physically-based control method. The dynamic cycle between the cyber and physical enhances growth of chatter in advance before it occurs, stabilizes the torque and chip-thickness variation and ensures stability of optimum cutting action. AISI 304 stainless steel experimental results show that the amplitude of vibration was reduced by 31 %, the energy efficiency of the spindle was increased by 22 %, and surface finishing was improved by 18 % over machining with constant parameters. The natural-language layer of interpretability translates twin-DRL decisions into insights in the form of operators, and the system aligns with the explainable and collaborative principles of Industry 5.0. The suggested model provides a single and dynamic route of intelligent micro-machining with predictive stability, minimized power usage, and transparent human-machine interaction.