Imperial College London
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DrCraigBuchanan

Faculty of Engineering, Department of Civil and Environmental Engineering

Senior Lecturer
 
 
 
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Contact

 

+44 (0)20 7594 8076craig.buchanan

 
 
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Location

 

247Skempton BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Dodwell:2021:10.1098/rspa.2021.0444,
author = {Dodwell, T and Flemming, L and Buchanan, C and Kyvelou, P and Detommasoe, G and Gosling, P and Scheichl, R and Kendall, W and Gardner, L and Girolami, M and Oates, C},
doi = {10.1098/rspa.2021.0444},
journal = {Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences},
title = {A data-centric approach to generative modelling for 3D-printed steel},
url = {http://dx.doi.org/10.1098/rspa.2021.0444},
volume = {477},
year = {2021}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - 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.
AU  - Dodwell,T
AU  - Flemming,L
AU  - Buchanan,C
AU  - Kyvelou,P
AU  - Detommasoe,G
AU  - Gosling,P
AU  - Scheichl,R
AU  - Kendall,W
AU  - Gardner,L
AU  - Girolami,M
AU  - Oates,C
DO  - 10.1098/rspa.2021.0444
PY  - 2021///
SN  - 1364-5021
TI  - A data-centric approach to generative modelling for 3D-printed steel
T2  - Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences
UR  - http://dx.doi.org/10.1098/rspa.2021.0444
UR  - http://hdl.handle.net/10044/1/92707
VL  - 477
ER  -
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