Digital Twin-Based Algorithms for Predictive Control of High-Precision CNC Machining Processes

This paper examines predictive control algorithms for high-precision machining processes on computer numerical control (CNC) machines, based on the concept of digital twins. The proposed approach uses a synchronized digital model of the process system to analyze the equipment’s condition, predict deviations, and adaptively adjust machining parameters in real time. The developed algorithms improve machining accuracy, reduce tool wear, and lower energy consumption, thereby enhancing the sustainability of production processes and reducing environmental impact. Particular attention is paid to issues of reliability, transparency, and the ethical use of AI technologies in the industrial environment. The results demonstrate the potential of digital twins as a tool for intelligent manufacturing and achieving sustainable development goals.