Data: A Design Framework for Adaptive Digital Twins
Date
2023-09-04 09:26
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Publisher
Cranfield University
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Citation
Erkoyuncu, John ahmet; Fernández del amo blanco, Iñigo; Ariansyah, Dedy; Bulka, Dominik; Vrabič, Rok; Roy, Rajkumar (2023). Data: A Design Framework for Adaptive Digital Twins. Cranfield Online Research Data (CORD). Dataset. https://doi.org/10.17862/cranfield.rd.12136074
Abstract
This paper develops a new DT design framework that uses ontologies to enable co-evolution with the CES by capturing data in terms of variety, velocity, and volume across the asset life-cycle. The framework has been tested successfully on a helicopter gearbox demonstrator and a mobile robotic system across their life cycles, illustrating DT adaptiveness without the data architecture needing to be modified. The data presented in this portal is related to the data that was generated in the validation process.
Description
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Github
Keywords
'Digital twins', 'Design method', 'Ontology', 'Computer Engineering', 'Engineering Practice', 'Manufacturing Robotics and Mechatronics (excl. Automotive Mechatronics)'
DOI
10.17862/cranfield.rd.12136074
Rights
CC BY 4.0