Validation of a digital twin : part distortion in heat treatment
Jayasinghe, Sarini and Hilton, Kareema and Mehnen, Jorn (2024) Validation of a digital twin : part distortion in heat treatment. MATEC Web of Conferences, 401. 13002. ISSN 2261-236X (https://doi.org/10.1051/matecconf/202440113002)
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Abstract
This paper presents a study on multi-process manufacturing optimisation, employing a sophisticated data analytic model based on Gaussian Processes. Through strategically designed aluminium trials and rigorous model evaluation using leave-one-out cross-validation, the efficacy of the model in identifying optimal parameters is demonstrated. An interactive dashboard, powered by the Anaconda Panel(c) library, enables real-time insights into the impact of varying parameters on outcomes. Experimental validation showcases the model's ability to reduce distortions yet highlights the dynamic nature of manufacturing processes. The iterative refinement of optimal parameters based on real-world observations underscores the adaptability of the model. This study emphasizes the importance of combining advanced data analytics with practical experimentation for enhanced precision in modern manufacturing, paving the way for further advancements in the field. Real-world validation of predicted optimal parameters reveals unexpected distortions, highlighting the dynamic nature of manufacturing processes and the necessity for continuous refinement. Further analysis uncovers a discrepancy in distortion outcomes, emphasizing the need for vigilant investigation and iterative refinement. By incorporating the latest experimental findings, the data analytic model evolves, offering updated optimal parameters and demonstrating practical applicability in minimizing part distortion effectively. This study underscores the significance of data-driven decision-making and the continuous integration of new experimental results in the pursuit of optimal manufacturing parameters, paving the way for further advancements in manufacturing optimisation.
ORCID iDs
Jayasinghe, Sarini
ORCID: https://orcid.org/0000-0003-4165-9496, Hilton, Kareema and Mehnen, Jorn
ORCID: https://orcid.org/0000-0001-6625-436X;
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Item type: Article ID code: 95391 Dates: DateEvent27 August 2024Published27 March 2024AcceptedSubjects: Technology > Manufactures Department: Faculty of Engineering > Design, Manufacture and Engineering Management > National Manufacturing Institute Scotland
Strategic Research Themes > Advanced Manufacturing and Materials
Faculty of Engineering > Design, Manufacture and Engineering ManagementDepositing user: Pure Administrator Date deposited: 23 Jan 2026 11:36 Last modified: 24 Jan 2026 01:22 URI: https://strathprints.strath.ac.uk/id/eprint/95391
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