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

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

Superalloys are a class of materials renowned for their exceptional ability to retain mechanical properties at elevated temperatures. Nickel superalloys, with a nickel content ranging from 38% to 76%, and iron–nickel superalloys (15–60% iron, 25–45% nickel) are extensively employed within the aviati...

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Bibliographic Details
Main Authors: Jaka Fajar Fatriansyah, Dzaky Iman Ajiputro, Agrin Febrian Pradana, Rio Sudwitama Persadanta Kaban, Andreas Federico, Muhammad Anis, Dedi Priadi, Nicolas Gascoin
Format: Article
Language:English
Published: MDPI AG 2025-05-01
Series:Metals
Subjects:
superalloys
nickel-based superalloys
machine learning
deep learning
material property prediction
Online Access:https://www.mdpi.com/2075-4701/15/5/565
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