{"id":{"repo_id":"wichita-thes","oai_identifier":"oai:soar.wichita.edu:10057/56126"},"canonical_url":"https://search.dev.ndltd.org/etd/wichita-thes/oai:soar.wichita.edu:10057/56126","repository":{"repo_id":"wichita-thes","name":"Wichita State University","base_url":"https://soar.wichita.edu/oai/request"},"display":{"title":"Digital twins and artificial intelligence for manufacturing systems","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.","abstract_html":"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_has_math":false,"creators":["Singh, Punit"],"institution":null,"degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2026,"date_issued":"2026-05","date_published":"2026-05","updated_at":"2026-07-24T06:06:46Z","subjects":[],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["hdl:10057/56126"],"render_values":[{"text":"hdl:10057/56126","href":null,"code":true}]}]},"links":{"outbound_url":null,"outbound_label":null,"outbound_source":null},"metadata_groups":[{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.issued","label":"Date","values":["2026-05"]},{"key":"dc:type","label":"Dc Type","values":["Dissertation"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["hdl:10057/56126"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.other","label":"Dc Description Other","values":["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."]},{"key":"dc:title","label":"Title","values":["Digital twins and artificial intelligence for manufacturing systems"]}]}],"canonical_facts":{"dc:date.issued":["2026-05"],"dc:description.other":["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."],"dc:identifier":["hdl:10057/56126"],"dc:title":["Digital twins and artificial intelligence for manufacturing systems"],"dc:type":["Dissertation"]},"updated_at":"2026-07-24T06:06:46Z"}