Dr BASEM ELSHAFEI BASEM.ELSHAFEI3@NOTTINGHAM.AC.UK
Research Fellow
Semantic Knowledge Representation in Asset Administration Shells: Empowering Manufacturing Utilization
Elshafei, Basem; Martínez-Arellano, Giovanna; Chaplin, Jack C.; Sanderson, David; Ratchev, Svetan
Authors
Mrs GIOVANNA MARTINEZ ARELLANO Giovanna.MartinezArellano@nottingham.ac.uk
ANNE MCLAREN RESEARCH FELLOW
Dr JACK CHAPLIN Jack.Chaplin@nottingham.ac.uk
ASSISTANT PROFESSOR
Dr David Sanderson DAVID.SANDERSON@NOTTINGHAM.AC.UK
CHIEF TECHNICAL OFFICER
Professor SVETAN RATCHEV svetan.ratchev@nottingham.ac.uk
CRIPPS PROFESSOR OF PRODUCTION ENGINEERING & HEAD OF RESEARCH DIVISION
Abstract
Within the context of Industry 4.0 and the Reference Architecture Model Industrie 4.0, the Asset Administration Shell (AAS) framework has emerged as a critical component for implementing Digital Twins that facilitate a seamless data exchange within a manufacturing ecosystem. The concept enables asset communication across various application domains, organizing the design and interaction amongst manufacturing components while capturing key information relating to assets such as operational parameters, intrinsic properties, and technical functionalities. Each category of information is stored within the AAS in a structure called a sub-model, while specific properties are stored as sub-model elements. Moreover, the literature presents a gap in post-processing the information contained within the sub-models to generate data-driven decisions and the bidirectional data exchange with the AAS. Additionally, the developing concept behind AAS requires demonstration with manufacturing cases of what can be done with the information in the submodels of the AAS. This research explores the practical implementation of AAS in a manufacturing context and the tools used in their development. An approach is developed to map a semantic ontol-ogy representation from the AAS structure. The semantic structure of the information enables querying and reasoning about the data, which contributes to better understanding among diverse manufacturing components , enabling enhanced monitoring and decision-making. The use case utilizes parameter data from the AAS to estimate a key performance Indicator, Overall Equipment Effectiveness (OEE), and stores the value back in the AAS. Utilizing this approach contributes to optimizing manufacturing efficiency and productivity within manufacturing production.
Citation
Elshafei, B., Martínez-Arellano, G., Chaplin, J. C., Sanderson, D., & Ratchev, S. (2024, June). Semantic Knowledge Representation in Asset Administration Shells: Empowering Manufacturing Utilization. Presented at 33rd International Conference on Flexible Automation and Intelligent Manufacturing (FAIM 2024), Taiwan, Taichung
Presentation Conference Type | Edited Proceedings |
---|---|
Conference Name | 33rd International Conference on Flexible Automation and Intelligent Manufacturing (FAIM 2024) |
Start Date | Jun 23, 2024 |
End Date | Jun 26, 2024 |
Acceptance Date | May 30, 2024 |
Online Publication Date | Dec 9, 2024 |
Publication Date | Dec 9, 2024 |
Deposit Date | Aug 5, 2024 |
Peer Reviewed | Peer Reviewed |
Pages | 282-296 |
Series Title | Lecture Notes in Mechanical Engineering |
Series ISSN | 2195-4364 |
Book Title | Flexible Automation and Intelligent Manufacturing: Manufacturing Innovation and Preparedness for the Changing World Order : Proceedings of FAIM 2024, June 23–26, 2024, Taichung, Taiwan, Volume 1 |
ISBN | 9783031744815 |
DOI | https://doi.org/10.1007/978-3-031-74482-2_32 |
Keywords | Asset Administration Shell; Semantic Ontology; Digital Twin; Key Performance Indicator |
Public URL | https://nottingham-repository.worktribe.com/output/38105325 |
Additional Information | First Online: 9 December 2024; Conference Acronym: FAIM; Conference Name: International Conference on Flexible Automation and Intelligent Manufacturing; Conference City: Taichung; Conference Country: Taiwan; Conference Year: 2024; Conference Start Date: 23 June 2024; Conference End Date: 26 June 2024; Conference ID: faim2024; Conference URL: https://www.faimconference.org/ |
External URL | https://www.faimconference.org/ |
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