Digital Twin–Driven Optimization of Smart Manufacturing Systems for Sustainable Production

Abstract

Smart manufacturing has become a cornerstone of modern industrial development, emphasizing flexibility, efficiency, and sustainability in production processes. Digital Twin technology has emerged as a transformative approach for modeling, monitoring, and optimizing manufacturing systems by creating a dynamic virtual representation of physical assets. This study presents a Digital Twin–driven optimization framework designed to enhance operational efficiency and environmental sustainability in smart manufacturing environments. The proposed framework integrates real-time sensor data, simulation models, and machine learning algorithms to create a continuously updated virtual model of production systems. This virtual model enables predictive analysis, operational optimization, and scenario-based decision making. A multi-layer architecture consisting of physical production systems, data acquisition modules, digital twin simulation models, and optimization algorithms is introduced. The framework utilizes reinforcement learning techniques to improve scheduling efficiency and reduce energy consumption. Experimental evaluation was conducted using a simulated manufacturing cell representing assembly line operations. Results indicate that the proposed digital twin system improves production throughput while reducing energy usage and machine idle time. Comparative analysis demonstrates that the framework achieves up to 21 percent improvement in production efficiency and approximately 17 percent reduction in energy consumption compared with conventional manufacturing optimization methods. The research highlights the potential of Digital Twin technology to support sustainable manufacturing practices and adaptive production management within Industry 4.0 environments.