{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,6]],"date-time":"2026-07-06T06:16:23Z","timestamp":1783318583575,"version":"3.54.6"},"reference-count":0,"publisher":"Advances in Artificial Intelligence and Machine Learning","issue":"03","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["AAIML"],"published-print":{"date-parts":[[2026,1,1]]},"abstract":"<jats:p>Reconfigurable Manufacturing Systems (RMS) are being utilised in smart manufacturing due to its ability to rapidly adjust its functionality and production in line with changes or fluctuations in market demands. The integration of Artificial Intelligence (AI) with Digital Twin (DT) offers a robust capability for real-time system\u2019s configuration, predictive analytics, process optimisation and decision-making in RMS. This study proposed an AI-DT framework for RMS to enable intelligent reconfiguration, adaptive control, continuous monitoring and machine learning (ML)-based predictive analytics. First, systematic literature review was employed to synthesis existing literature on the applications of AI and DT to identify research gaps and foster their integration within the RMS environment. Secondly, a framework that leverages AI, Internet of Things (IoT) and cloud computing was proposed to process high-volume sensor data to enable effective system\u2019s reconfiguration in real time. The validation of the proposed AI-DT model conducted in the Python environment indicated that the model can achieved up to 35% increase throughput and 55% reduction in downtime compared to the baseline model. Furthermore, the proposed intelligent model achieved 48% improvement in response time compared to the baseline. The findings obtained in this study suggest that integrated AI-DT model can significantly promote the agility and resilience of RMS in smart manufacturing. The findings of this study are useful in the exploration of AI-DT models for enhancing the capabilities of the RMS.<\/jats:p>","DOI":"10.54364\/aaiml.2026.63303","type":"journal-article","created":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T11:22:23Z","timestamp":1779276143000},"page":"5480","source":"Crossref","is-referenced-by-count":0,"title":["An Artificial Intelligence Driven Digital Twins Framework for Reconfigurable Manufacturing Systems: Towards Integration, Adaptability and Productivity"],"prefix":"10.54364","volume":"06","author":[{"given":"Michael","family":"Edili Ogbaje","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gregory","family":"Onwodi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Felix","family":"Ale","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Oludolapo Akanni","family":"Olanrewaju","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kazeem","family":"Aderemi Bello","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rendani Wilson","family":"Maladzhi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lanre","family":"Daniyan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ilesanmi","family":"Daniyan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"32807","published-online":{"date-parts":[[2026,1,1]]},"container-title":["Advances in Artificial Intelligence and Machine Learning"],"original-title":[],"link":[{"URL":"https:\/\/www.oajaiml.com\/uploads\/archivepdf\/943863303.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,6]],"date-time":"2026-07-06T05:53:38Z","timestamp":1783317218000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.oajaiml.com\/uploads\/archivepdf\/943863303.pdf"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,1]]},"references-count":0,"journal-issue":{"issue":"03","published-online":{"date-parts":[[2026,1,1]]},"published-print":{"date-parts":[[2026,1,1]]}},"URL":"https:\/\/doi.org\/10.54364\/aaiml.2026.63303","relation":{},"ISSN":["2582-9793"],"issn-type":[{"value":"2582-9793","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,1]]}}}