Digital Twin Technology for Process Optimization and Smart Manufacturing Systems

Abstract

Digital twin technology—a real-time, high-fidelity virtual replica of physical assets, processes, or systems—has emerged as a transformative enabler of smart manufacturing within Industry 4.0. By integrating data from IoT sensors, simulation models, and analytics, digital twins support real-time monitoring, predictive maintenance, process optimization, and design validation. This study explores the application of digital twins in manufacturing systems, focusing on their role in process optimization, predictive maintenance, quality control, and sustainable production. We synthesize findings from foundational studies and industrial surveys to discern key capabilities that drive successful deployment, such as adaptability, scalability, and interoperability. A notable case includes a digital twin–based deep reinforcement learning framework for smart forging processes, achieving reduced temperature unevenness and improved control policy automation arXiv. Additionally, a modeling framework for self-adaptive manufacturing leverages domain expertise to enable digital twins to adjust configurations under environmental changes arXiv. A workflow framework is also outlined, encompassing physical-virtual integration, twin data loops, and optimization cycles ScienceDirectMDPISpringerOpen. Key advantages include enhanced operational efficiency, reduced downtime, improved product quality, and accelerated innovation cycles. Conversely, challenges revolve around data integration, standardization gaps, cybersecurity, and expertise shortages arXivResearchGateMDPI. The study concludes that digital twins offer rich potential for process optimization and smart manufacturing. Future developments should prioritize unified frameworks, federated twin integration across suppliers, AI-driven analytics, and lifecycle-aware models to fully unlock industrial digital transformation.