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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2025-05-01
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| author | Jaka Fajar Fatriansyah Dzaky Iman Ajiputro Agrin Febrian Pradana Rio Sudwitama Persadanta Kaban Andreas Federico Muhammad Anis Dedi Priadi Nicolas Gascoin |
| author_facet | Jaka Fajar Fatriansyah Dzaky Iman Ajiputro Agrin Febrian Pradana Rio Sudwitama Persadanta Kaban Andreas Federico Muhammad Anis Dedi Priadi Nicolas Gascoin |
| author_sort | Jaka Fajar Fatriansyah |
| collection | DOAJ |
| description | 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 aviation industry due to their resilience in harsh operating environments. These components encounter extreme temperatures during operation, significantly impacting their tensile strength and melting point. Furthermore, high-speed rotation and abrasive conditions necessitate materials with superior hardness. Consequently, material modifications are crucial to ensure that gas turbine components meet their required properties. Machine learning (ML) and deep learning (DL) offer promising solutions for the design of materials with tailored tensile strength, hardness, and melting point properties. This study investigates the efficacy of direct and inverse machine learning models in predicting crucial material properties and composition, respectively. The model with the most favorable prediction accuracy is identified through the systematic variation of key parameters. The findings show that a fully connected feed-forward Artificial Neural Network (ANN) with three hidden layers using ReLU activation functions performs better than the other models. This capability is leveraged to modify the composition of INCONEL-718, successfully achieving significant enhancements in tensile strength (1592 MPa), hardness (152 HRB), and melting point (1665 °C). |
| format | Article |
| id | doaj-art-dcd429da0f054f94936567c812f403d6 |
| institution | DOAJ |
| issn | 2075-4701 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Metals |
| spelling | doaj-art-dcd429da0f054f94936567c812f403d62025-08-20T03:14:32ZengMDPI AGMetals2075-47012025-05-0115556510.3390/met15050565Physical Property Prediction of High-Temperature Nickel and Iron–Nickel Superalloys Using Direct and Inverse Composition Machine Learning ModelsJaka Fajar Fatriansyah0Dzaky Iman Ajiputro1Agrin Febrian Pradana2Rio Sudwitama Persadanta Kaban3Andreas Federico4Muhammad Anis5Dedi Priadi6Nicolas Gascoin7Department of Metallurgical and Materials Engineering, Faculty of Engineering, Universitas Indonesia, Depok 16424, West Java, IndonesiaDepartment of Metallurgical and Materials Engineering, Faculty of Engineering, Universitas Indonesia, Depok 16424, West Java, IndonesiaDepartment of Metallurgical and Materials Engineering, Faculty of Engineering, Universitas Indonesia, Depok 16424, West Java, IndonesiaDepartment of Metallurgical and Materials Engineering, Faculty of Engineering, Universitas Indonesia, Depok 16424, West Java, IndonesiaDepartment of Metallurgical and Materials Engineering, Faculty of Engineering, Universitas Indonesia, Depok 16424, West Java, IndonesiaDepartment of Metallurgical and Materials Engineering, Faculty of Engineering, Universitas Indonesia, Depok 16424, West Java, IndonesiaDepartment of Metallurgical and Materials Engineering, Faculty of Engineering, Universitas Indonesia, Depok 16424, West Java, IndonesiaINSA Centre Val de Loire, University of Orléans, PRISME, UR4229, F-18020 Bourges, FranceSuperalloys 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 aviation industry due to their resilience in harsh operating environments. These components encounter extreme temperatures during operation, significantly impacting their tensile strength and melting point. Furthermore, high-speed rotation and abrasive conditions necessitate materials with superior hardness. Consequently, material modifications are crucial to ensure that gas turbine components meet their required properties. Machine learning (ML) and deep learning (DL) offer promising solutions for the design of materials with tailored tensile strength, hardness, and melting point properties. This study investigates the efficacy of direct and inverse machine learning models in predicting crucial material properties and composition, respectively. The model with the most favorable prediction accuracy is identified through the systematic variation of key parameters. The findings show that a fully connected feed-forward Artificial Neural Network (ANN) with three hidden layers using ReLU activation functions performs better than the other models. This capability is leveraged to modify the composition of INCONEL-718, successfully achieving significant enhancements in tensile strength (1592 MPa), hardness (152 HRB), and melting point (1665 °C).https://www.mdpi.com/2075-4701/15/5/565superalloysnickel-based superalloysmachine learningdeep learningmaterial property prediction |
| spellingShingle | Jaka Fajar Fatriansyah Dzaky Iman Ajiputro Agrin Febrian Pradana Rio Sudwitama Persadanta Kaban Andreas Federico Muhammad Anis Dedi Priadi Nicolas Gascoin Physical Property Prediction of High-Temperature Nickel and Iron–Nickel Superalloys Using Direct and Inverse Composition Machine Learning Models Metals superalloys nickel-based superalloys machine learning deep learning material property prediction |
| title | Physical Property Prediction of High-Temperature Nickel and Iron–Nickel Superalloys Using Direct and Inverse Composition Machine Learning Models |
| title_full | Physical Property Prediction of High-Temperature Nickel and Iron–Nickel Superalloys Using Direct and Inverse Composition Machine Learning Models |
| title_fullStr | Physical Property Prediction of High-Temperature Nickel and Iron–Nickel Superalloys Using Direct and Inverse Composition Machine Learning Models |
| title_full_unstemmed | Physical Property Prediction of High-Temperature Nickel and Iron–Nickel Superalloys Using Direct and Inverse Composition Machine Learning Models |
| title_short | Physical Property Prediction of High-Temperature Nickel and Iron–Nickel Superalloys Using Direct and Inverse Composition Machine Learning Models |
| title_sort | physical property prediction of high temperature nickel and iron nickel superalloys using direct and inverse composition machine learning models |
| topic | superalloys nickel-based superalloys machine learning deep learning material property prediction |
| url | https://www.mdpi.com/2075-4701/15/5/565 |
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