Digital twin model for performance analysis and manufacturing process optimization

Abstract This study aims to develop a Digital Twin-based framework to address these challenges, with a practical application to heavy vehicle components. The proposed approach is characterized by the integration of planning data, operational parameters, and mathematical models into a unified digital platform that enables real-time monitoring and prediction. The results showed that the proposed system improved process performance prediction accuracy by 12.6%, reduced material waste by 9.4%, and reduced processing time by 7.8%, while achieving a significant improvement in the quality of the final surface. These indicators confirm that integrating Digital Twins into computer-aided manufacturing not only increases operational efficiency but also expands the potential for smart manufacturing by enhancing the reliability of digital modeling and supporting informed decision in complex industrial environments.