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dc.contributor.authorErichsen, Jorgen Falck
dc.contributor.authorSjöman, Heikki
dc.contributor.authorSteinert, Martin
dc.contributor.authorWelo, Torgeir
dc.date.accessioned2022-03-08T10:20:52Z
dc.date.available2022-03-08T10:20:52Z
dc.date.created2021-06-23T14:38:37Z
dc.date.issued2021
dc.identifier.citationArtificial intelligence for engineering design, analysis and manufacturing. 2021, 35 (1), 65-80.en_US
dc.identifier.issn0890-0604
dc.identifier.urihttps://hdl.handle.net/11250/2983732
dc.description.abstractAiming to help researchers capture output from the early stages of engineering design projects, this article presents a new research tool for digitally capturing physical prototypes. The motivation for this work is to collect observations that can aid in understanding prototyping in the early stages of engineering design projects, and this article investigates if and how digital capture of physical prototypes can be used for this purpose. Early-stage prototypes are usually rough and of low fidelity and are thus often discarded or substantially modified through the projects. Hence, retrospective access to prototypes is a challenge when trying to gather accurate empirical data. To capture the prototypes developed through the early stages of a project, a new research tool has been developed for capturing prototypes through multi-view images, along with metadata describing by whom, why, when, and where the prototypes were captured. Over the course of 17 months, this research tool has been used to capture more than 800 physical prototypes from 76 individual users across many projects. In this article, one project is shown in detail to demonstrate how this capturing system can gather empirical data for enriching engineering design project cases that focus on prototyping for concept generation. The authors also analyze the metadata provided by the system to give understanding into prototyping patterns in the projects. Lastly, through enabling digital capture of large quantities of data, the research tool presents the foundations for training artificial intelligence-based predictors and classifiers that can be used for analysis in engineering design research.en_US
dc.language.isoengen_US
dc.publisherCambridge University Pressen_US
dc.rightsNavngivelse 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.no*
dc.titleProtobooth: Gathering and analyzing data on prototyping in early-stage engineering design projects by digitally capturing physical prototypesen_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.description.versionpublishedVersionen_US
dc.source.pagenumber65-80en_US
dc.source.volume35en_US
dc.source.journalArtificial intelligence for engineering design, analysis and manufacturingen_US
dc.source.issue1en_US
dc.identifier.doi10.1017/S0890060420000414
dc.identifier.cristin1917978
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1


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