Systems Development for the Industrial IoT: Challenges from Industry R&amp;D Projects

Santos, Nuno; Morais, Francisco; Rodrigues, Helena; Machado, Ricardo J.

doi:10.1007/978-3-030-24892-5_3

“…In smart manufacturing, streaming data acquired from IIoT sensors continuously need to be processed so that they can be used for machine learning. Thus, the scalability of a reference architecture [12,25] remains a key challenge in this context as delayed or incorrect recommendations of the AI systems to manufacturing operators may delay the workflow or, even worse, can also endanger their safety. In this context we research on appropriate consistency requirements in the face of data sharding between different replicas to better cope with typical data volumes in this context.…”

Section: Research Challengesmentioning

confidence: 99%

Towards a Reference Software Architecture for Human-AI Teaming in Smart Manufacturing

Haindl¹,

Buchgeher²,

Khan³

et al. 2022

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With the proliferation of AI-enabled software systems in smart manufacturing, the role of such systems moves away from a reactive to a proactive role that provides context-specific support to manufacturing operators. In the frame of the EU funded Teaming.AI project, we identified the monitoring of teaming aspects in human-AI collaboration, the runtime monitoring and validation of ethical policies, and the support for experimentation with data and machine learning algorithms as the most relevant challenges for human-AI teaming in smart manufacturing. Based on these challenges, we developed a reference software architecture based on knowledge graphs, tracking and scene analysis, and components for relational machine learning with a particular focus on its scalability. Our approach uses knowledge graphs to capture productand process specific knowledge in the manufacturing process and to utilize it for relational machine learning. This allows for contextspecific recommendations for actions in the manufacturing process for the optimization of product quality and the prevention of physical harm. The empirical validation of this software architecture will be conducted in cooperation with three large-scale companies in the automotive, energy systems, and precision machining domain. In this paper we discuss the identified challenges for such a reference software architecture, present its preliminary status, and sketch our further research vision in this project.

show abstract

Towards a Reference Software Architecture for Human-AI Teaming in Smart Manufacturing

Haindl

¹

,

Buchgeher

²

,

Khan³

et al. 2022

2022 IEEE/ACM 44th International Conference on Software Engineering: New Ideas and Emerging Results (ICSE-NIER)

View full text Add to dashboard Cite

Towards a reference software architecture for human-AI teaming in smart manufacturing

Haindl

¹

,

Buchgeher

²

,

Khan³

et al. 2022

Proceedings of the ACM/IEEE 44th International Conference on Software Engineering: New Ideas and Emerging Results

5

0

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With the proliferation of AI-enabled software systems in smart manufacturing, the role of such systems moves away from a reactive to a proactive role that provides context-specific support to manufacturing operators. In the frame of the EU funded Teaming.AI project, we identified the monitoring of teaming aspects in human-AI collaboration, the runtime monitoring and validation of ethical policies, and the support for experimentation with data and machine learning algorithms as the most relevant challenges for human-AI teaming in smart manufacturing. Based on these challenges, we developed a reference software architecture based on knowledge graphs, tracking and scene analysis, and components for relational machine learning with a particular focus on its scalability. Our approach uses knowledge graphs to capture productand process specific knowledge in the manufacturing process and to utilize it for relational machine learning. This allows for contextspecific recommendations for actions in the manufacturing process for the optimization of product quality and the prevention of physical harm. The empirical validation of this software architecture will be conducted in cooperation with three large-scale companies in the automotive, energy systems, and precision machining domain. In this paper we discuss the identified challenges for such a reference software architecture, present its preliminary status, and sketch our further research vision in this project.

show abstract

Systems Development for the Industrial IoT: Challenges from Industry R&D Projects

Cited by 4 publications

References 17 publications

Towards a Reference Software Architecture for Human-AI Teaming in Smart Manufacturing

Towards a Reference Software Architecture for Human-AI Teaming in Smart Manufacturing

Towards a Reference Software Architecture for Human-AI Teaming in Smart Manufacturing

Towards a reference software architecture for human-AI teaming in smart manufacturing

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