Digital twin-enabled collaborative data management for metal additive manufacturing systems

Liu, Chao, Le Roux, Leopold, Körner, Carolin, Tabaste, Olivier, Lacan, Franck ORCID: https://orcid.org/0000-0002-3499-5240 and Bigot, Samuel ORCID: https://orcid.org/0000-0002-0789-4727 2022. Digital twin-enabled collaborative data management for metal additive manufacturing systems. Journal of Manufacturing Systems 62 , pp. 857-874. 10.1016/j.jmsy.2020.05.010

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Abstract

Metal Additive Manufacturing (AM) has been attracting a continuously increasing attention due to its great advantages compared to traditional subtractive manufacturing in terms of higher design flexibility, shorter development time, lower tooling cost, and fewer production wastes. However, the lack of process robustness, stability and repeatability caused by the unsolved complex relationships between material properties, product design, process parameters, process signatures, post AM processes and product quality has significantly impeded its broad acceptance in the industry. To facilitate efficient implementation of advanced data analytics in metal AM, which would support the development of intelligent process monitoring, control and optimisation, this paper proposes a novel Digital Twin (DT)-enabled collaborative data management framework for metal AM systems, where a Cloud DT communicates with distributed Edge DTs in different product lifecycle stages. A metal AM product data model that contains a comprehensive list of specific product lifecycle data is developed to support the collaborative data management. The feasibility and advantages of the proposed framework are validated through the practical implementation in a distributed metal AM system developed in the project MANUELA. A representative application scenario of cloud-based and deep learning-enabled metal AM layer defect analysis is also presented. The proposed DT-enabled collaborative data management has shown great potential in enhancing fundamental understanding of metal AM processes, developing simulation and prediction models, reducing development times and costs, and improving product quality and production efficiency.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Engineering
Publisher:	Elsevier
ISSN:	0278-6125
Funders:	Horizon2020
Date of First Compliant Deposit:	27 May 2020
Date of Acceptance:	18 May 2020
Last Modified:	06 Nov 2023 21:19
URI:	https://orca.cardiff.ac.uk/id/eprint/131937

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