Digital twins and artificial intelligence for manufacturing systems
type: CreativeWork
description: 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.
abstract: 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.
author: Singh, Punit
date published: 2026-05
date modified: 2026-07-24T06:06:46Z
provider: Wichita State University
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: Wichita State University