Production Value Networks Use Cases for NVIDIA AI Microservices Integration in the Eclipse Arrowhead Framework

Modern industrial systems face significant challenges in scalability, interoperability, and secure orchestration, particularly in industries like aerospace, automotive, and manufacturing. This paper proposes a novel integration of NVIDIA Metropolis microservices with the Eclipse Arrowhead Framework to address these challenges. By combining advanced AI capabilities with secure service-oriented frameworks, the proposed architecture enables real-time data processing, predictive maintenance, and resource optimization across distributed industrial environments. Customized for critical industrial applications, this modular and scalable approach demonstrates enhanced operational efficiency, safety, and adaptability, aligning with Industry 5.0 principles. The paper lays a strongfoundation for implementing these solutions in real-world settings and advancing the capabilities of AI-driven industrial systems.

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Publication Details

Published
2025-03-11
DOI
https://doi.org/10.1109/icin64016.2025.10942762
Citations
2
Primary Topic
Digital Transformation in Industry
Type
article
Field-Weighted Citation Impact
2.48
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article

Production Value Networks Use Cases for NVIDIA AI Microservices Integration in the Eclipse Arrowhead Framework

Eduard-Cristian Popovici, Alexandru Vulpe, Cosmina Stalidi, Octavian Fratu et al.
2 citations
Digital Transformation in Industry
2.48
article

Production Value Networks Use Cases for NVIDIA AI Microservices Integration in the Eclipse Arrowhead Framework

Eduard-Cristian Popovici, Alexandru Vulpe, Cosmina Stalidi, Octavian Fratu, George Suciu
article en
2 citations

Abstract

Modern industrial systems face significant challenges in scalability, interoperability, and secure orchestration, particularly in industries like aerospace, automotive, and manufacturing. This paper proposes a novel integration of NVIDIA Metropolis microservices with the Eclipse Arrowhead Framework to address these challenges. By combining advanced AI capabilities with secure service-oriented frameworks, the proposed architecture enables real-time data processing, predictive maintenance, and resource optimization across distributed industrial environments. Customized for critical industrial applications, this modular and scalable approach demonstrates enhanced operational efficiency, safety, and adaptability, aligning with Industry 5.0 principles. The paper lays a strongfoundation for implementing these solutions in real-world settings and advancing the capabilities of AI-driven industrial systems.

Universitatea Națională de Știință și Tehnologie Politehnica București (RO)
Industry, innovation and infrastructure
Openalex Percentile: Top 11%
Digital Transformation in Industry
2.48
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