Show simple item record

dc.contributor.authorDodwell, TJ
dc.contributor.authorFleming, LR
dc.contributor.authorBuchanan, C
dc.contributor.authorKyvelou, P
dc.contributor.authorDetommaso, G
dc.contributor.authorGosling, PD
dc.contributor.authorScheichl, R
dc.contributor.authorKendall, WS
dc.contributor.authorGardner, L
dc.contributor.authorGirolami, Mark
dc.contributor.authorOates, CJ
dc.date.accessioned2021-11-12T16:47:22Z
dc.date.available2021-11-12T16:47:22Z
dc.date.issued2021-11
dc.date.submitted2021-05-31
dc.identifier.issn1364-5021
dc.identifier.otherrspa20210444
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/330582
dc.description.abstractThe 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.
dc.languageen
dc.publisherThe Royal Society
dc.subjectResearch articles
dc.subject3D printing
dc.subjectBayesian uncertainty quantification
dc.subjectelastoplasticity
dc.subjectprobabilistic mechanics
dc.subjectstochastic finite elements
dc.titleA data-centric approach to generative modelling for 3D-printed steel.
dc.typeArticle
dc.date.updated2021-11-12T16:47:22Z
prism.issueIdentifier2255
prism.publicationNameProc Math Phys Eng Sci
prism.volume477
dc.identifier.doi10.17863/CAM.78026
dcterms.dateAccepted2021-10-08
rioxxterms.versionofrecord10.1098/rspa.2021.0444
rioxxterms.versionAO
rioxxterms.versionVoR
rioxxterms.licenseref.urihttp://creativecommons.org/licenses/by/4.0/
dc.contributor.orcidDodwell, TJ [0000-0003-0408-200X]
dc.contributor.orcidKendall, WS [0000-0001-9799-3480]
dc.contributor.orcidGardner, L [0000-0003-0126-6807]
dc.contributor.orcidGirolami, Mark [0000-0003-3008-253X]
dc.identifier.eissn1471-2946
pubs.funder-project-idUK Research and Innovation (2TAFFP/100007)
cam.issuedOnline2021-11-10


Files in this item

Thumbnail
Thumbnail

This item appears in the following Collection(s)

Show simple item record