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A data-centric approach to generative modelling for 3D-printed steel
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Dodwell, T. J., Fleming, L. R., Buchanan, C., Kyvelou, P., Detommaso, G., Gosling, P.D., Scheichl, R., Kendall, W. S., Gardner, L. (Leroy), Girolami, M.A. and Oates, C. J. (2021) A data-centric approach to generative modelling for 3D-printed steel. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 477 (2255). 20210444. doi:10.1098/rspa.2021.0444 ISSN 1364-5021.
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Official URL: https://doi.org/10.1098/rspa.2021.0444
Abstract
The emergence of additive manufacture (AM) for metallic material enables components of near arbitrary complexity to be produced. This has potential to disrupt traditional engineering approaches. However, metallic AM components exhibit greater levels of variation in their geometric and mechanical properties compared to standard components, which is not yet well understood. This uncertainty poses a fundamental barrier to potential users of the material, since extensive post-manufacture testing is currently required to ensure safety standards are met. Taking an interdisciplinary approach that combines probabilistic mechanics and uncertainty quantification, we demonstrate that intrinsic variation in AM steel can be well described by a generative statistical model that enables the quality of a design to be predicted before manufacture. Specifically, the geometric variation in the material can be described by an anisotropic spatial random field with oscillatory covariance structure, and the mechanical behaviour by a stochastic anisotropic elasto-plastic material model. The fitted generative model is validated on a held-out experimental dataset and our results underscore the need to combine both statistical and physics-based modelling in the characterization of new AM steel products.
Item Type: | Journal Article | |||||||||||||||
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Subjects: | Q Science > QA Mathematics T Technology > TA Engineering (General). Civil engineering (General) T Technology > TS Manufactures |
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Divisions: | Faculty of Science, Engineering and Medicine > Science > Statistics | |||||||||||||||
Library of Congress Subject Headings (LCSH): | Additive manufacturing , Three-dimensional printing, Bayesian statistical decision theory, Elastoplasticity, Engineering -- Statistical methods , Mechanics, Applied -- Statistical methods, Probabilities | |||||||||||||||
Journal or Publication Title: | Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences | |||||||||||||||
Publisher: | The Royal Society Publishing | |||||||||||||||
ISSN: | 1364-5021 | |||||||||||||||
Official Date: | 24 November 2021 | |||||||||||||||
Dates: |
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Volume: | 477 | |||||||||||||||
Number: | 2255 | |||||||||||||||
Article Number: | 20210444 | |||||||||||||||
DOI: | 10.1098/rspa.2021.0444 | |||||||||||||||
Status: | Peer Reviewed | |||||||||||||||
Publication Status: | Published | |||||||||||||||
Access rights to Published version: | Open Access (Creative Commons) | |||||||||||||||
Date of first compliant deposit: | 9 November 2021 | |||||||||||||||
Date of first compliant Open Access: | 11 November 2021 | |||||||||||||||
RIOXX Funder/Project Grant: |
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