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A Smart Manufacturing Ecosystem for Industry 5.0 using Cloud-based Collaborative Learning at the Edge
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.ORCID iD: 0000-0001-9118-5861
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.ORCID iD: 0000-0002-2123-8187
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.ORCID iD: 0000-0003-3874-9968
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.ORCID iD: 0000-0001-6158-3543
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2023 (English)In: NOMS 2023-2023 IEEE/IFIP Network Operations and Management Symposium / [ed] Kemal Akkaya, Olivier Festor, Carol Fung, Mohammad Ashiqur Rahman, Lisandro Zambenedetti Granville, Carlos Raniery Paula dos Santos, IEEE, 2023Conference paper, Published paper (Refereed)
Abstract [en]

In the modern manufacturing industry, collaborative architectures are growing in popularity. We propose an Industry 5.0 value-driven manufacturing process automation ecosystem in which each edge automation system is based on a local cloud and has a service-oriented architecture. Additionally, we integrate cloud-based collaborative learning (CCL) across building energy management, logistic robot management, production line management, and human worker Aide local clouds to facilitate shared learning and collaborate in generating manufacturing workflows. Consequently, the workflow management system generates the most effective and Industry 5.0-driven workflow recipes. In addition to managing energy for a sustainable climate and executing a cost-effective, optimized, and resilient manufacturing process, this work ensures the well-being of human workers. This work has significant implications for future work, as the ecosystem can be deployed and tested for any industrial use case.

Place, publisher, year, edition, pages
IEEE, 2023.
Series
IEEE/IFIP Network Operations and Management Symposium, ISSN 1542-1201, E-ISSN 2374-9709
Keywords [en]
Industry 5.0, Smart Manufacturing Ecosystem, Eclipse Arrowhead Framework, Value-driven Automation, Local Cloud-based Architecture, AI at the Edge, Collaborative Learning
National Category
Other Mechanical Engineering
Research subject
Cyber-Physical Systems; Machine Learning
Identifiers
URN: urn:nbn:se:ltu:diva-96939DOI: 10.1109/NOMS56928.2023.10154323Scopus ID: 2-s2.0-85164738175ISBN: 978-1-6654-7717-8 (print)ISBN: 978-1-6654-7716-1 (electronic)OAI: oai:DiVA.org:ltu-96939DiVA, id: diva2:1753051
Conference
IEEE/IFIP Network Operations and Management Symposium, May 8–12, 2023, Miami, USA
Note

European Commission, Arrowhead Tools project (ECSEL JU, No.826452)

Available from: 2023-04-25 Created: 2023-04-25 Last updated: 2023-10-11Bibliographically approved

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Javed, SalmanJaved, Salehavan Deventer, JanMokayed, HamamDelsing, Jerker

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