research-article

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

Authors:

Philipp Haindl,

Georg Buchgeher,

Bernhard MoserAuthors Info & Claims

ICSE-NIER '22: Proceedings of the ACM/IEEE 44th International Conference on Software Engineering: New Ideas and Emerging Results

Pages 96 - 100

https://doi.org/10.1145/3510455.3512788

Published: 17 October 2022 Publication History

Abstract

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 product-and process specific knowledge in the manufacturing process and to utilize it for relational machine learning. This allows for context-specific 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.

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

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cover image ACM Conferences

ICSE-NIER '22: Proceedings of the ACM/IEEE 44th International Conference on Software Engineering: New Ideas and Emerging Results

May 2022

143 pages

ISBN:9781450392242

DOI:10.1145/3510455

Conference Chairs:
Liliana Pasquale
University College Dublin and Lero, Ireland
,
Christoph Treude
University of Melbourne, Australia

Copyright © 2022 ACM.

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Published: 17 October 2022

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ICSE '22: 44th International Conference on Software Engineering

May 21 - 29, 2022

Pennsylvania, Pittsburgh

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Cited By

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https://doi.org/10.3390/info15110728
Mohan JRanganathan PReza H(2024)Hierarchical Software Framework for Safe Unmanned Aerial Systems Integration into National Airspace (NAS)2024 IEEE 14th Annual Computing and Communication Workshop and Conference (CCWC)10.1109/CCWC60891.2024.10427776(0198-0206)Online publication date: 8-Jan-2024
https://doi.org/10.1109/CCWC60891.2024.10427776
Rajesh ASharma AChinthaginjala RPau GAbbas MShankar TFouziya Sulthana S(2024)Enhancing visual clarity in rainy conditions based on single-frame filtering algorithmAin Shams Engineering Journal10.1016/j.asej.2024.10284615:8(102846)Online publication date: Aug-2024
https://doi.org/10.1016/j.asej.2024.102846
Hoch TMartinez-Gil JPichler MSilvina AHeinzl BMoser BEleftheriou DEstrada-Lugo HLeva M(2023)Multi-Stakeholder Perspective on Human-AI Collaboration in Industry 5.0Artificial Intelligence in Manufacturing10.1007/978-3-031-46452-2_23(407-421)Online publication date: 28-Sep-2023
https://doi.org/10.1007/978-3-031-46452-2_23
Sathish KAnbazhagan RVenkata RArena FPau G(2022)Investigation and Numerical Simulation of the Acoustic Target Strength of the Underwater Submarine VehicleInventions10.3390/inventions70401117:4(111)Online publication date: 1-Dec-2022
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Khan MAhmad ASobieczky FPichler MMoser BBukovsky I(2022)A Systematic Mapping Study of Predictive Maintenance in SMEsIEEE Access10.1109/ACCESS.2022.320069410(88738-88749)Online publication date: 2022
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