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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 (AUTHOR) d.g.ca@ieee.org, Tucker, Conrad2,3 (AUTHOR) conradt@andrew.cmu.edu, Beuth, Jack1,2 (AUTHOR)
Source: Journal of Intelligent Manufacturing. Jun2026, Vol. 37 Issue 6, p2203-2214. 12p.
Database: Business Source Ultimate
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PubType: Academic Journal
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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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              Text: Jun2026
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              Y: 2026
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              Value: 37
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