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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
Show others and affiliations
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: 2024-12-09Bibliographically approved
In thesis
1. Data-Driven Optimizations in Production Value Networks
Open this publication in new window or tab >>Data-Driven Optimizations in Production Value Networks
2025 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Industry 4.0 is transforming traditional production systems into dynamic and adaptive value networks. However, this shift reveals significant challenges in achieving seamless interoperability, workflow optimization, and effective value chain analysis within complex production environments. This thesis contributes to addressing these challenges through a structured approach, supporting data-driven and adaptive decision-making to optimize operations in dynamic production value networks.

The research commences by architecting microservice-oriented systems that facilitate the integration of legacy and brownfield technologies with Industry 4.0-compliant environments. By leveraging the Eclipse Arrowhead framework, the thesis demonstrates how diverse systems can exchange data and collaborate at runtime, establishing the foundation for cohesive and interoperable production networks.

Building on this interoperable structure, the thesis explores AI-driven optimizations across key areas, including workflow optimization, predictive maintenance, and demand response. These approaches support operational efficiency and adaptability in production value networks. Case studies showcase collaborative learning models for condition monitoring and an edge-based framework to optimize energy use, demonstrating tangible improvements in efficiency and resilience.

Finally, a significant contribution of this thesis is the introduction of tools for visualization and analysis of value chains. Using the Reference Architectural Model for Industry 4.0 (RAMI 4.0), the research provides methods to map and evaluate value creation within dynamic production networks. By integrating activity-based costing with microservice architectures, it offers granular insights into cost and value dynamics at runtime, enabling agile and informed decision-making in complex industrial environments.

Through these contributions, the thesis advances the understanding and implementation of data-driven optimizations in production value networks, supporting agility and sustainability while contributing to the transition to circular business models by enabling value chain analysis across the product lifecycle. The thesis serves as a bridge between theoretical frameworks and practical applications, providing valuable insights for both academia and industry, and paving the way for more efficient and sustainable production ecosystems.

Place, publisher, year, edition, pages
Luleå tekniska universitet, 2025
Keywords
Industry 4.0, Smart Industry Ecosystems, Microservice Architecture, Lifecycle Management, Run-time Value Chain Analysis, Real-time Decision Making, Production Value Networks, Stakeholder Collaboration
National Category
Engineering and Technology
Research subject
Cyber-Physical Systems
Identifiers
urn:nbn:se:ltu:diva-111004 (URN)978-91-8048-716-0 (ISBN)978-91-8048-717-7 (ISBN)
Public defence
2025-02-26, A117, Luleå University of Technology, Luleå, 09:00 (English)
Opponent
Supervisors
Projects
Arrowhead fPVNAI REDGIO 5.0
Funder
European Commission
Available from: 2024-12-09 Created: 2024-12-09 Last updated: 2025-01-30Bibliographically approved

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

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