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AdditiveGDL: generative deep learning for predicting local thermal distributions in metal 3D-printed layers.

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Title: AdditiveGDL: generative deep learning for predicting local thermal distributions in metal 3D-printed layers.
Authors: Guirguis, David1,2, d.g.ca@ieee.org, Tucker, Conrad2,3, conradt@andrew.cmu.edu, Beuth, Jack1,2
Source: Journal of Intelligent Manufacturing; Jun2026, Vol. 37 Issue 6, p2203-2214, 12p
Database: Applied Science & Technology Source
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Header DbId: aci
DbLabel: Applied Science & Technology Source
An: 193785490
AccessLevel: 2
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Intelligent+Manufacturing%22">Journal of Intelligent Manufacturing</searchLink>; Jun2026, Vol. 37 Issue 6, p2203-2214, 12p
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=aci&AN=193785490
RecordInfo BibRecord:
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      – Type: doi
        Value: 10.1007/s10845-025-02640-2
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      – Code: eng
        Text: English
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        PageCount: 12
        StartPage: 2203
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      – TitleFull: AdditiveGDL: generative deep learning for predicting local thermal distributions in metal 3D-printed layers.
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            NameFull: Guirguis, David
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            NameFull: Tucker, Conrad
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            NameFull: Beuth, Jack
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            – D: 01
              M: 06
              Text: Jun2026
              Type: published
              Y: 2026
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              Value: 37
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