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Physical Property Prediction of High-Temperature Nickel and Iron–Nickel Superalloys Using Direct and Inverse Composition Machine Learning Models.

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Title: Physical Property Prediction of High-Temperature Nickel and Iron–Nickel Superalloys Using Direct and Inverse Composition Machine Learning Models.
Authors: Fatriansyah, Jaka Fajar1, jakafajar@ui.ac.id, Ajiputro, Dzaky Iman1,2, Pradana, Agrin Febrian1, Kaban, Rio Sudwitama Persadanta1,2, Federico, Andreas1, Anis, Muhammad1, Priadi, Dedi1, Gascoin, Nicolas2
Source: Metals (2075-4701); May2025, Vol. 15 Issue 5, p565, 13p
Database: Applied Science & Technology Source
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Header DbId: aci
DbLabel: Applied Science & Technology Source
An: 185474556
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  Data: Physical Property Prediction of High-Temperature Nickel and Iron–Nickel Superalloys Using Direct and Inverse Composition Machine Learning Models.
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  Data: <searchLink fieldCode="JN" term="%22Metals+%282075-4701%29%22">Metals (2075-4701)</searchLink>; May2025, Vol. 15 Issue 5, p565, 13p
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=aci&AN=185474556
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        Value: 10.3390/met15050565
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      – Code: eng
        Text: English
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        PageCount: 13
        StartPage: 565
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      – TitleFull: Physical Property Prediction of High-Temperature Nickel and Iron–Nickel Superalloys Using Direct and Inverse Composition Machine Learning Models.
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              Text: May2025
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