In recent years, Digital Twin (DT) technology has emerged as a transformative force, reshaping the landscape of manufacturing optimization across various industries. A DT is a virtual representation of a physical system, process, or product that enables real-time monitoring, analysis, and decision-making. This innovative technology has opened new horizons for predictive maintenance, Machine Learning (ML) and Deep Learning (DL) applications, modeling and simulation techniques, reference architectures, big data-driven strategies, and the integration of IoT and edge architectures in the smart manufacturing sector.
This special issue aims to explore the dynamic evolution of DT applications in manufacturing optimization, but not limited to. We seek to provide a comprehensive understanding of how DTs are revolutionizing industries such as healthcare, railways, aerospace, and beyond. Moreover, we will delve into the intricacies of ML and DL techniques, modeling and simulation advances, reference architectures for optimizing manufacturing processes, the role of big data in predictive maintenance, and the synergy between IoT and edge architectures in the era of Industry 4.0.
Contributions to this special issue will shed light on the latest breakthroughs and best practices, enabling researchers, engineers, and practitioners to harness the full potential of digital twins in revolutionizing the manufacturing domain. We encourage submissions that demonstrate innovative solutions and real-world case studies that showcase the transformative power of digital twins in manufacturing optimization.


Main Topics:
We invite researchers, experts, and practitioners to submit original research, review articles, and case studies that align with the following main topics within the scope of digital twin technology for manufacturing optimization:
• Digital Twins for Predictive Maintenance: Explore how digital twins are impacting predictive maintenance practices in industries, including healthcare, railways, aerospace, and more.
• Leveraging and Optimization Machine Learning and Deep Learning: Investigate the utilization of Machine Learning and Deep Learning algorithms to develop, enhance, and employ digital twins in manufacturing systems.
• Innovative Modeling and Simulation Techniques: Showcase innovative modeling and simulation techniques that support the development and application of digital twins in manufacturing.
• Reference Architectures for Manufacturing Optimization: Present best practices and reference architectures for optimizing manufacturing systems and processes using digital twins.
• Model-Driven and Data Driven Predictive Maintenance: Delve into the integration of data analytics into predictive maintenance strategies within digital twin-enabled manufacturing processes.
• IoT and Edge Architectures in Digital Twin Optimization: Explore the integration and effectiveness of IoT and edge architectures in harnessing the power of digital twins for manufacturing optimization.
• Case study for design optimization using the DT approach: Investigate DT applications in smart manufacturing sector.