AdditiveGDL: Generative deep learning for predicting local thermal distributions in metal 3D-printed layers Article Swipe
David Guirguis
,
Conrad S. Tucker
,
Jack Beuth
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-5257658/v1
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-5257658/v1
In this paper, we propose a method that leverages constrained generative neural networks to predict the local thermal distribution given the laser toolpath. By identifying critical regions of heat accumulation, we can optimize geometry, and scan paths, ultimately enhancing the quality and reliability of 3D-printed metal components. Thermal distribution and dynamics affect various critical aspects including microstructure and mechanical properties, fatigue life, residual stresses, dimensional accuracy, shape integrity, and surface quality. Additionally, the scan strategy significantly impacts heat accumulation, especially at geometric features with sharp corners, overhangs, and thin walls. Thus, predicting heat distribution given the scan strategy becomes crucial. While numerical simulations using finite element methods are common, they can be computationally prohibitive for complex parts. Results show that generative deep learning offers an alternative approach to predicting the thermal field of printed layers efficiently.
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Metadata
- Type
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- Language
- en
- Landing Page
- https://doi.org/10.21203/rs.3.rs-5257658/v1
- OA Status
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- 10
- OpenAlex ID
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All OpenAlex metadata
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https://openalex.org/W4403461988Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.21203/rs.3.rs-5257658/v1Digital Object Identifier
- Title
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AdditiveGDL: Generative deep learning for predicting local thermal distributions in metal 3D-printed layersWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-10-16Full publication date if available
- Authors
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David Guirguis, Conrad S. Tucker, Jack BeuthList of authors in order
- Landing page
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https://doi.org/10.21203/rs.3.rs-5257658/v1Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.21203/rs.3.rs-5257658/v1Direct OA link when available
- Concepts
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Generative grammar, 3d printed, Thermal, Deep learning, Artificial intelligence, Generative model, Computer science, Materials science, Geography, Engineering, Meteorology, Manufacturing engineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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