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Digital Twins, Extended Reality, and Artificial Intelligence in Manufacturing Reconfiguration: A Systematic Literature Review

Article dans une revue avec comité de lecture
Author
ccMAYER, Anjela
469668 Institut für Informationsmanagement im Ingenieurwesen [IMI]
GREIF, Lucas
469668 Institut für Informationsmanagement im Ingenieurwesen [IMI]
HÄUSSERMANN, Tim Markus
309952 Hochschule Mannheim - University of Applied Sciences
ccOTTO, Simon
150665 Karlsruhe Institute of Technology = Karlsruher Institut für Technologie [KIT]
KASTNER, Kevin
309952 Hochschule Mannheim - University of Applied Sciences
EL BOBBOU, Sleiman
469668 Institut für Informationsmanagement im Ingenieurwesen [IMI]
ccCHARDONNET, Jean-Rémy
543315 Laboratoire d’Ingénierie des Systèmes Physiques et Numériques [LISPEN]
REICHWALD, Julian
309952 Hochschule Mannheim - University of Applied Sciences
ccFLEISCHER, Jürgen
150665 Karlsruhe Institute of Technology = Karlsruher Institut für Technologie [KIT]
ccOVTCHAROVA, Jivka
469668 Institut für Informationsmanagement im Ingenieurwesen [IMI]

URI
http://hdl.handle.net/10985/26185
DOI
10.3390/su17052318
Date
2025-03-06
Journal
Sustainability

Abstract

This review draws on a systematic literature review and bibliometric analysis to examine how Digital Twins (DTs), Extended Reality (XR), and Artificial Intelligence (AI) support the reconfiguration of Cyber–Physical Systems (CPSs) in modern manufacturing. The review aims to provide an updated overview of these technologies’ roles in CPS reconfiguration, summarize best practices, and suggest future research directions. In a two-phase process, we first analyzed related work to assess the current state of assisted manufacturing reconfiguration and identify gaps in existing reviews. Based on these insights, an adapted PRISMA methodology was applied to screen 165 articles from the Scopus and Web of Science databases, focusing on those published between 2019 and 2025 addressing DT, XR, and AI integration in Reconfigurable Manufacturing Systems (RMSs). After applying the exclusion criteria, 38 articles were selected for final analysis. The findings highlight the individual and combined impact of DTs, XR, and AI on reconfiguration processes. DTs notably reduce reconfiguration time and improve system availability, AI enhances decision-making, and XR improves human–machine interactions. Despite these advancements, a research gap exists regarding the combined application of these technologies, indicating potential areas for future exploration. The reviewed studies recognized limitations, especially due to diverse study designs and methodologies that may introduce risks of bias, yet the review offers insight into the current DT, XR, and AI landscape in RMS and suggests areas for future research.

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