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DC poleHodnotaJazyk
dc.contributor.authorEilermann, Sebastian
dc.contributor.authorPetroll, Christoph
dc.contributor.authorHoefer, Philipp
dc.contributor.authorNiggemann, Oliver
dc.contributor.editorSkala, Václav
dc.date.accessioned2024-07-31T17:52:47Z-
dc.date.available2024-07-31T17:52:47Z-
dc.date.issued2024
dc.identifier.citationWSCG 2024: full papers proceedings: 32. International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, p. 207-216.en
dc.identifier.issn2464–4625 (online)
dc.identifier.issn2464–4617 (print)
dc.identifier.urihttp://hdl.handle.net/11025/57400
dc.description.sponsorshipThis research as part of the project LaiLa is funded by dtec.bw - Digitalization and Technology Reasearch Center of the Bundeswehr which we gratefully ac knowledge. dtec.bw is funded by the European Union - NextGenerationEU.en
dc.format10 scs
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.publisherVáclav Skala - UNION Agencyen
dc.rights© Václav Skala - UNION Agencyen
dc.subject3D generacecs
dc.subjectmultikriteriálnícs
dc.subjectoptimalizacecs
dc.subjectdržák motorucs
dc.subjectpodmíněný variační autokodércs
dc.subjectsimulace založené na hodnocenícs
dc.title3D Multi-Criteria Design Generation and Optimization of an Engine Mount for an Unmanned Air Vehicle Using a Conditional Variational Autoencoderen
dc.typekonferenční příspěvekcs
dc.typeconferenceObjecten
dc.rights.accessopenAccessen
dc.type.versionpublishedVersionen
dc.description.abstract-translatedOne of the most promising developments in computer vision in recent years is the use of generative neural net works for functionality condition-based 3D design reconstruction and generation. Here, neural networks learn dependencies between functionalities and a geometry in a very effective way. For a neural network the function alities are translated in conditions to a certain geometry. But the more conditions the design generation needs to reflect, the more difficult it is to learn clear dependencies. This leads to a multi criteria design problem due various conditions, which are not considered in the neural network structure so far. In this paper, we address this multi-criteria challenge for a 3D design use case related to an unmanned aerial vehicle (UAV) motor mount. We generate 10,000 abstract 3D designs and subject them all to simulations for three physical disciplines: mechanics, thermodynamics, and aerodynamics. Then, we train a Conditional Variational Autoencoder (CVAE) using the geometry and corresponding multicriteria functional constraints as input. We use our trained CVAE as well as the Marching cubes algorithm to generate meshes for simulation based evaluation. The results are then evaluated with the generated UAV designs. Subsequently, we demonstrate the ability to generate optimized designs under self-defined functionality conditions using the trained neural networken
dc.subject.translated3D Generationen
dc.subject.translatedmulti-criteriaen
dc.subject.translatedoptimizationen
dc.subject.translatedengine mounten
dc.subject.translatedconditional variational autoencoderen
dc.subject.translatedsimulation based evaluationen
dc.identifier.doihttps://doi.org/10.24132/10.24132/CSRN.3401.22
dc.type.statusPeer revieweden
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