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Design based on availability: Generative design and robotic fabrication workflow for non-standardized sheet metal with variable properties

dc.date.accessioned2022-08-16T09:47:00Z
dc.date.available2022-08-16T09:47:00Z
dc.date.issued2022-03-07
dc.identifierdoi:10.17170/kobra-202206016280
dc.identifier.urihttp://hdl.handle.net/123456789/14060
dc.description.sponsorshipGefördert im Rahmen des Projekts DEALger
dc.language.isoeng
dc.rightsNamensnennung-Nicht-kommerziell 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/*
dc.subjectreuseeng
dc.subjectcomputational design algorithmeng
dc.subjectrobotic foldingeng
dc.subjectmulti-objective optimizationeng
dc.subjectform-findingeng
dc.subjectsearch algorithmgeng
dc.subjectmachine learningeng
dc.subjectsensing/scanningeng
dc.subjectmaterial classificationeng
dc.subject.ddc004
dc.subject.ddc600
dc.titleDesign based on availability: Generative design and robotic fabrication workflow for non-standardized sheet metal with variable propertieseng
dc.typeAufsatz
dcterms.abstractThis paper presents the use of a computational design algorithm in combination with robotic fabrication and sensing to augment the design and construction process for non-standardized material. Although reusing reclaimed material can significantly reduce the environmental impact in building construction, current design processes are not set up for this shift in thinking. Contrary to conventional practices, designing within the constraints of available material means that geometry and topology cannot be fully pre-determined. This paper introduces a design methodology for corrugated shell structures from folded sheet metal of variable geometries and properties, in which the design goal adapts to available material. It follows a two-fold approach of digital algorithm development and scanning and physical prototyping for robotic fabrication. The scanned materials database is classified based on object geometry data and material properties; such as thickness, type of metal, and spring-back values for fabrication purposes. Together with a target surface, it is an input for a generative design algorithm consisting of surface generation and optimization. The surface generation tries to approximate the target through a translation of search algorithm results for object placement into a 2D mesh graph which is then linked to 3D particle spring based form-finding. The optimization consists of evaluation of structural, fabrication, and design criteria, with finally user selection. Robotic fabrication included object recognition, metal sheet folding and consideration of different metal spring back behavior. These methods were tested on a single curved arch surface and applied to a double curved cantilever canopy as a final demonstrator. The algorithm results showed a generation of different corrugated shell topologies based on iterated object placement. As a demonstrator, a part of the selected canopy was robotically fabricated from local industrial leftovers.eng
dcterms.accessRightsopen access
dcterms.creatorMoussavi, Seyed Mobin
dcterms.creatorSvatoš-Ražnjević, Hana
dcterms.creatorKörner, Axel
dcterms.creatorTahouni, Yasaman
dcterms.creatorMenges, Achim
dcterms.creatorKnippers, Jan
dc.relation.doidoi:10.1177/09560599221081104
dc.subject.swdRobotikger
dc.subject.swdAlgorithmusger
dc.subject.swdMetallverarbeitungger
dc.subject.swdWiederverwendungger
dc.subject.swdKreislaufwirtschaftger
dc.subject.swdBauwirtschaftger
dc.subject.swdEffizienzger
dc.type.versionpublishedVersion
dcterms.source.identifiereissn:2059-8033
dcterms.source.issueIssue 2
dcterms.source.journalInternational Journal of Space Structureseng
dcterms.source.pageinfo119-134
dcterms.source.volumeVolume 37
kup.iskupfalse


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