Digital twins and artificial intelligence for manufacturing systems
Digital twin (DT) technology and artificial intelligence are revolutionizing manufacturing with advanced digital engineering tools and intelligent models that facilitate simulation, analysis, monitoring, control, and forecasting of real-world manufacturing systems and processes. Manufacturers benefit from successful digital twin modeling and simulation that allow them to verify and validate the impacts of planning and decision-making before their actual implementation. This reduces the costs associated with redesign, redevelopment, deployment failures, real-time optimization, and more. In digital manufacturing, more accurate virtual production line models that accurately reflect real-world systems and processes are required to close gaps between design and actual operation. This study proposes a framework for analytical decoupling based on digital twins to facilitate intelligent decision-making in production system design and evaluation. Accordingly, this work demonstrates the framework on modeling and implementation of an intelligent system for real-time quality control with physics-informed digital twins and machine learning for sheet metal production lines. In addition, this study reviews digital-twin-and-artificial-intelligence-based (DT-AI-based) solutions for advancing production scheduling via digital transformation. With regard to prediction, monitoring, and diagnosis, our case studies demonstrate the potential benefits of the presented DT-AI-based solutions for advancing production systems and manufacturing.
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