Physical Property Prediction of High-Temperature Nickel and Iron–Nickel Superalloys Using Direct and Inverse Composition Machine Learning Models
Abstract
1. Introduction
2. Materials and Methods
3. Results and Discussion
3.1. Performance of Composition-to-Properties (C2P) Predictions
3.2. Performance of Properties-to-Composition (P2C) Predictions
3.3. Feature Importance
3.4. Study Case
4. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Nickel Superalloy | |||||
---|---|---|---|---|---|
Feature | Unit | Mean | Min | Max | Std. Dev |
C | wt.% | 0.08 | 0.03 | 0.15 | 0.03 |
Si | wt.% | 0.16 | 0.01 | 0.49 | 0.13 |
Mn | wt.% | 0.16 | 0 | 0.49 | 0.17 |
P | wt.% | 0 | 0 | 0.01 | 0 |
S | wt.% | 0.01 | 0 | 0.02 | 0 |
Ni | wt.% | 64.29 | 46.9 | 75.63 | 10.36 |
Cr | wt.% | 16.52 | 12.2 | 21.4 | 2.57 |
Mo | wt.% | 3.22 | 0 | 9 | 2.77 |
Cu | wt.% | 0.02 | 0 | 0.16 | 0.04 |
Al | wt.% | 2.29 | 0 | 6.3 | 2.11 |
N | wt.% | 0.02 | 0 | 0.28 | 0.06 |
B | wt.% | 0.01 | 0 | 0.02 | 0 |
Nb + Ta | wt.% | 0.67 | 0 | 2.69 | 0.92 |
Co | wt.% | 7.68 | 0 | 28.88 | 9.98 |
Fe | wt.% | 03.02 | 0 | 9.26 | 3.47 |
Ti | wt.% | 1.64 | 0.01 | 3.47 | 1.25 |
W | wt.% | 0.19 | 0 | 2.61 | 0.55 |
Zr | wt.% | 0.02 | 0 | 0.15 | 0.04 |
Nb | wt.% | 0.04 | 0 | 0.97 | 0.19 |
Test Temperature | °C | 586.65 | 25 | 1000 | 303.79 |
Tensile Strength | MPa | 716.08 | 70 | 1348 | 299.05 |
Hardness | HRB | 101.01 | 73 | 112 | 12.11 |
Melting Point | °C | 1341.43 | 1230 | 1413 | 54.24 |
Elongation | % | 29.45 | 0 | 127 | 28.28 |
Reduction of Area | % | 35.31 | 1 | 99 | 27.14 |
0.2% Proof Stress | MPa | 471.06 | 38 | 878 | 253.12 |
Iron–Nickel Superalloy | |||||
---|---|---|---|---|---|
Feature | Unit | Mean | Min | Max | Std. Dev |
C | wt.% | 0.12 | 0.02 | 0.43 | 0.12 |
Si | wt.% | 0.56 | 0.14 | 0.94 | 0.18 |
Mn | wt.% | 1.28 | 0.83 | 1.91 | 0.28 |
P | wt.% | 0.01 | 0.01 | 0.02 | 0 |
S | wt.% | 0.01 | 0 | 0.02 | 0 |
Ni | wt.% | 27.39 | 19.73 | 34.45 | 5.77 |
Cr | wt.% | 19.24 | 0.88 | 21.52 | 4.15 |
Mo | wt.% | 1.37 | 0 | 4.26 | 1.59 |
Cu | wt.% | 0.09 | 0 | 0.34 | 0.1 |
Al | wt.% | 0.43 | 0.01 | 5 | 0.95 |
N | wt.% | 0.05 | 0.01 | 0.19 | 0.06 |
B | wt.% | 0.02 | 0 | 0.5 | 0.09 |
V | wt.% | 0.03 | 0 | 0.32 | 0.09 |
Ti | wt.% | 0.45 | 0 | 2.16 | 0.61 |
Co | wt.% | 7.15 | 0 | 20.1 | 9.36 |
Nb + Ta | wt.% | 0.77 | 0 | 4.48 | 1.38 |
W | wt.% | 1.1 | 0 | 4.23 | 1.56 |
Fe | wt.% | 39.93 | 24.22 | 64.74 | 9.96 |
Test Temperature | wt.% | 493.53 | 25 | 1000 | 289.99 |
Tensile Strength | wt.% | 492.33 | 58 | 1100 | 215.71 |
Hardness | wt.% | 86.97 | 70 | 108 | 11.03 |
Melting Point | wt.% | 1324.86 | 1230 | 1450 | 76.86 |
Elongation | wt.% | 45.13 | 10 | 129 | 23.15 |
Reduction of Area | wt.% | 53.04 | 13 | 97 | 20.72 |
0.2% Proof Stress | wt.% | 244.17 | 35 | 812 | 155 |
Nickel Superalloy | |||
---|---|---|---|
Properties | ANN | KNN | SVR |
Tensile Strength | 0.91 ± 0.04 | 0.81 ± 0.02 | 0.82 ± 0.01 |
Hardness | 0.94 ± 0.02 | 0.94 ± 0.04 | 0.90 ± 0.01 |
Melting Point | 0.94 ± 0.03 | 0.92 ± 0.03 | 0.91 ± 0.03 |
Iron–nickel Superalloy | |||
Properties | ANN | KNN | SVR |
Tensile Strength | 0.91 ± 0.06 | 0.72 ± 0.04 | 0.81 ± 0.03 |
Hardness | 0.92 ± 0.03 | 0.94 ± 0.02 | 0.9 ± 0.03 |
Melting Point | 0.91 ± 0.08 | 0.97 ± 0.01 | 0.9 ± 0.03 |
Composition | Model | ||
---|---|---|---|
ANN | KNN | SVR | |
Nickel Superalloy | |||
C | 0.82 ± 0.07 | 0.75 ± 0.04 | 0.70 ± 0.01 |
Si | 0.71 ± 0.08 | 0.52 ± 0.04 | 0.68 ± 0.01 |
Mn | 0.88 ± 0.06 | 0.87 ± 0.02 | 0.91 ± 0.03 |
P | 0.72 ± 0.09 | 0.65 ± 0.02 | 0.65 ± 0.02 |
Ni | 0.95 ± 0.03 | 0.95 ± 0.03 | 0.91 ± 0.02 |
Cr | 0.91 ± 0.04 | 0.87 ± 0.04 | 0.82 ± 0.01 |
Mo | 0.95 ± 0.08 | 0.95 ± 0.02 | 0.93 ± 0.01 |
Cu | 0.53 ± 0.07 | 0.51 ± 0.01 | 0.44 ± 0.01 |
Al | 0.94 ± 0.09 | 0.95 ± 0.02 | 0.91 ± 0.02 |
N | 0.81 ± 0.09 | 0.97 ± 0.01 | 0.67 ± 0.02 |
B | 0.71 ± 0.07 | 0.65 ± 0.02 | 0.66 ± 0.02 |
Nb + Ta | 0.94 ± 0.05 | 0.95 ± 0.01 | 0.92 ± 0.02 |
Co | 0.96 ± 0.03 | 0.94 ± 0.01 | 0.91 ± 0.02 |
Fe | 0.93 ± 0.03 | 0.94 ± 0.02 | 0.91 ± 0.02 |
Ti | 0.94 ± 0.03 | 0.9389 ± 0.02 | 0.9658 ± 0.02 |
W | 0.96 ± 0.02 | 0.8255 ± 0.03 | 0.9345 ± 0.02 |
Zr | 0.91 ± 0.04 | 0.8799 ± 0.05 | 0.887 ± 0.06 |
Nb | 0.95 ± 0.03 | 0.95 ± 0.03 | 0.9235 ± 0.01 |
Iron–nickel Superalloy | |||
C | 0.974 ± 0.01 | 0.96 ± 0.01 | 0.90 ± 0.04 |
Si | 0.80 ± 0.06 | 0.70 ± 0.19 | 0.72 ± 0.11 |
Mn | 0.74 ± 0.07 | 0.72 ± 0.10 | 0.74 ± 0.09 |
P | 0.30 ± 0.19 | 0.3119 ± 0.23 | 0.35 ± 0.12 |
S | 0.34 ± 0.24 | 0.52 ± 0.12 | 0.42 ± 0.14 |
Ni | 0.94 ± 0.01 | 0.95 ± 0.01 | 0.91 ± 0.01 |
Cr | 0.82 ± 0.10 | 0.71 ± 0.09 | 0.41 ± 0.05 |
Mo | 0.96 ± 0.01 | 0.94 ± 0.01 | 0.91 ± 0.01 |
Cu | 0.55 ± 0.23 | 0.61 ± 0.22 | 0.61 ± 0.12 |
Al | 0.25 ± 0.12 | 0.26 ± 0.23 | 0.43 ± 0.09 |
N | 0.92 ± 0.03 | 0.91 ± 0.01 | 0.91 ± 0.03 |
Nb + Ta | 0.97 ± 0.01 | 0.96 ± 0.01 | 0.91 ± 0.04 |
Co | 0.95 ± 0.02 | 0.96 ± 0.01 | 0.92 ± 0.04 |
Fe | 0.91 ± 0.02 | 0.91 ± 0.01 | 0.85 ± 0.09 |
Ti | 0.95 ± 0.02 | 0.95 ± 0.01 | 0.92 ± 0.03 |
W | 0.95 ± 0.03 | 0.94 ± 0.01 | 0.91 ± 0.03 |
V | 0.93 ± 0.02 | 0.94 ± 0.02 | 0.93 ± 0.02 |
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Fatriansyah, J.F.; Ajiputro, D.I.; Pradana, A.F.; Kaban, R.S.P.; Federico, A.; Anis, M.; Priadi, D.; Gascoin, N. Physical Property Prediction of High-Temperature Nickel and Iron–Nickel Superalloys Using Direct and Inverse Composition Machine Learning Models. Metals 2025, 15, 565. https://doi.org/10.3390/met15050565
Fatriansyah JF, Ajiputro DI, Pradana AF, Kaban RSP, Federico A, Anis M, Priadi D, Gascoin N. Physical Property Prediction of High-Temperature Nickel and Iron–Nickel Superalloys Using Direct and Inverse Composition Machine Learning Models. Metals. 2025; 15(5):565. https://doi.org/10.3390/met15050565
Chicago/Turabian StyleFatriansyah, Jaka Fajar, Dzaky Iman Ajiputro, Agrin Febrian Pradana, Rio Sudwitama Persadanta Kaban, Andreas Federico, Muhammad Anis, Dedi Priadi, and Nicolas Gascoin. 2025. "Physical Property Prediction of High-Temperature Nickel and Iron–Nickel Superalloys Using Direct and Inverse Composition Machine Learning Models" Metals 15, no. 5: 565. https://doi.org/10.3390/met15050565
APA StyleFatriansyah, J. F., Ajiputro, D. I., Pradana, A. F., Kaban, R. S. P., Federico, A., Anis, M., Priadi, D., & Gascoin, N. (2025). Physical Property Prediction of High-Temperature Nickel and Iron–Nickel Superalloys Using Direct and Inverse Composition Machine Learning Models. Metals, 15(5), 565. https://doi.org/10.3390/met15050565