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dc.contributor.authorBrion, Douglas
dc.contributor.authorShen, M
dc.contributor.authorPattinson, Sebastian
dc.date.accessioned2022-04-19T23:31:05Z
dc.date.available2022-04-19T23:31:05Z
dc.date.issued2022-08
dc.identifier.issn2214-8604
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/336241
dc.description.abstractWarp deformation is a common error encountered in additive manufacturing. It is typically caused by residual internal stresses in the manufactured part that arise as material cools. These errors are challenging to prevent or correct as they build over time and thus are only visible long after the actions that caused them. As a result, existing work in extrusion additive man- ufacturing has attempted warp detection but not correction or prevention. We report a hybrid approach combining deep learning, computer vision, and expert heuristics to correct or prevent warp. We train a deep convolutional neural network using diverse labelled images to recognise warp in real-time. We compute five metrics from detection candidates to predict the severity of warp deformation and proportionately update print settings. This enables the first demon- stration of automated warp detection and correction both during printing and for future prints.
dc.description.sponsorshipThis work has been funded by the Engineering and Physical Sciences Research Council (EP- SRC) PhD Studentship EP/N509620/1 to Douglas Brion, Royal Society award RGS/R2/192433 to Sebastian Pattinson, Academy of Medical Sciences award SBF005/1014 to Sebastian Pattin- 600 son, Engineering and Physical Sciences Research Council award EP/V062123/1 to Sebastian Pattinson, and An Isaac Newton Trust award to Sebastian Pattinson.
dc.publisherElsevier BV
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.titleAutomated recognition and correction of warp deformation in extrusion additive manufacturing
dc.typeArticle
dc.publisher.departmentDepartment of Engineering
dc.date.updated2022-04-19T10:01:31Z
prism.publicationNameAdditive Manufacturing
dc.identifier.doi10.17863/CAM.83660
dcterms.dateAccepted2022-04-18
rioxxterms.versionofrecord10.1016/j.addma.2022.102838
rioxxterms.versionAM
dc.contributor.orcidBrion, Douglas [0000-0002-5361-2882]
dc.contributor.orcidPattinson, Sebastian [0000-0002-7851-7718]
dc.identifier.eissn2214-8604
rioxxterms.typeJournal Article/Review
pubs.funder-project-idAcademy of Medical Sciences (SBF005\1014)
pubs.funder-project-idEngineering and Physical Sciences Research Council (EP/V062123/1)
pubs.funder-project-idEngineering and Physical Sciences Research Council (2274909)
pubs.funder-project-idEngineering and Physical Sciences Research Council (EP/N509620/1)
cam.orpheus.successWed Jun 08 08:57:13 BST 2022 - Embargo updated*
cam.orpheus.counter2
cam.depositDate2022-04-19
pubs.licence-identifierapollo-deposit-licence-2-1
pubs.licence-display-nameApollo Repository Deposit Licence Agreement
rioxxterms.freetoread.startdate2023-08-31


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Attribution-NonCommercial-NoDerivatives 4.0 International
Except where otherwise noted, this item's licence is described as Attribution-NonCommercial-NoDerivatives 4.0 International