Real-Time Digital Twin-Driven Optimization of Industrial Machinery

Industry 4.0 has significantly transformed manufacturing by integrating advanced digital technologies, and challenges remain in achieving accurate real-time monitoring and process enhancement. Traditional industrial systems commonly struggle with inefficiencies due to the lack of precise virtual representations that can mirror physical machines in real-time. This gap has led to increased operational costs, reduced productivity, and limited adaptability in automated manufacturing. To address this issue, this research explores the creation and execution of digital twins for industrial machinery, offering a solution to optimize performance and decision-making. The suggested approach utilizes Siemens NX Mechatronic Concept Design (MCD) software with Siemens Programmable Logic Controller (PLC) S7-1200 to create high-fidelity virtual models that replicate real machine behavior, while the Open Platform Communications (OPC) protocol facilitates seamless real-time data transfer between virtual and real systems. To determine the accuracy and dependability of the developed digital twins, twelve experimental tests are conducted under identical conditions for both physical and virtual models. The results demonstrate a high level of precision, with an average error of 0.66%. These findings highlight the capability of digital twins in advancing smart manufacturing, enhancing process control, and fostering innovation in industrial automation.