Data-Driven Modeling and Optimization of Heat Treatment Parameters for Aluminum Alloys Using Advanced Machine Learning
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OpenAlex topics: Aluminum Alloy Microstructure Properties · Additive Manufacturing Materials and Processes · Metallurgy and Material Forming
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References (37)
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1. R. Casati, J.N. Lemke, A.Z. Alarcon et al., Aging Behavior Of High-Strength Al Alloy 2618 Produced by Selective Laser Melting[J], Metall. Mater. Trans. A, 2017, 48, p 575–579.10.1007/s11661-016-3883-y ↗
2. M. Bartošák, I. Šulák, J. Horváth et al., Isothermal Low-Cycle Fatigue and Fatigue–Creep Behaviour of 2618 Aluminium Alloy, Int. J. Fatigue, 2024, 179, 108027.10.1016/j.ijfatigue.2023.108027 ↗
3. F. Belelli, R. Casati, F. Larini et al., Investigation on Two Ti-B-Reinforced Al Alloys for Laser Powder Bed Fusion, Mater. Sci. Eng. A, 2021, 808, 140944.10.1016/j.msea.2021.140944 ↗
4. M. Schuster, A. De Luca, D. Kucajda et al., Heat Treatment Response and Mechanical Properties of a Zr-Modified AA2618 Aluminum Alloy Fabricated by Laser Powder Bed Fusion, J. Alloys Compd., 2023, 962, 171166.10.1016/j.jallcom.2023.171166 ↗
5. N. Patel, M. Joshi, A. Singh et al., Effect of Solution Heat Treatment (temperature and time) on Microstructure and Properties of Al-Cu-Mg Alloy[J], Int. J. Metalcast., 2024, 18(2), p 1151–1159.10.1007/s40962-023-01095-6 ↗
6. Z. Zhu, J. Chen, C. Wu et al., Effects of Melt Titanium Content on the Grain Structure and Mechanical Properties of Al-5Ti-B-Modified Alloy 2618[J], Int. J. Cast Met. Res., 2022, 35(5–6), p 206–217.10.1080/13640461.2023.2177784 ↗
7. E.M. Elgallad, P. Shen, Z. Zhang et al., Effects of Heat Treatment on the Microstructure and Mechanical Properties of AA2618 DC Cast Alloy[J], Mater. Des., 2014, 61, p 133–140.10.1016/j.matdes.2014.04.045 ↗
8. Y. Ma, P. Xu, M. Li et al., The Mastery of Details in the Workflow of Materials Machine Learning, NPJ Comput. Mater., 2024, 10(1), 141.10.1038/s41524-024-01331-5 ↗
9. P. Xu, X. Ji, M. Li, and W. Lu, Small Data Machine Learning in Materials Science[J], npj Comput. Mater., 2023, 9(1), p 42.10.1038/s41524-023-01000-z ↗
10. Y. Liu, Z. Yang, X. Zou et al., Data Quantity Governance for Machine Learning in Materials Science, Natl. Sci. Rev., 2023, 10(7), nwad125.10.1093/nsr/nwad125 ↗
11. A. Roy, A. Hussain, P. Sharma et al., Rapid Discovery of High Hardness Multi-Principal-Element Alloys Using a Generative Adversarial Network Model, Acta Mater., 2023, 257, 119177.10.1016/j.actamat.2023.119177 ↗
12. S.P. Padhy, V. Chaudhary, Y.F. Lim et al., Experimentally Validated Inverse Design of Multi-Property Fe-Co-Ni Alloys, iScience, 2024 https://doi.org/10.1016/j.isci.2024.10972310.1016/j.isci.2024.109723 ↗
13. J.F. Fatriansyah, M.R.R. Satrio, A. Federico et al., Machine Learning-Based Forward and Inverse Designs for Prediction and Optimization of Fracture Toughness of Aluminum Alloy[J], Results Eng., 2024, 23, 102717.10.1016/j.rineng.2024.102717 ↗
14. S. BallupeteNagaraju, M. KodigarahalliSomashekara, M. Puttegowda et al., Effect of B4C/Gr on Hardness and Wear Behavior of Al2618 Based Hybrid Composites Through Taguchi and Artificial Neural Network Analysis[J], Catalysts, 2022, 12(12), p 1654.10.3390/catal12121654 ↗
15. H. Kneiding and D. Balcells, Augmenting Genetic Algorithms with Machine Learning for Inverse Molecular Design[J], Chem. Sci., 2024, 15(38), p 15522–15539.10.1039/D4SC02934H ↗
16. L. Jiang, C. Wang, H. Fu et al., Discovery of Aluminum Alloys With Ultra-Strength and High-Toughness via a Property-Oriented Design Strategy[J], J. Mater. Sci. Technol., 2022, 98, p 33–43.10.1016/j.jmst.2021.05.011 ↗
17. X. Wang, V. Snášel, S. Mirjalili et al., Artificial Protozoa Optimizer (APO): A Novel Bio-Inspired Metaheuristic Algorithm for Engineering Optimization, Knowl.-Based Syst., 2024, 295, 111737.10.1016/j.knosys.2024.111737 ↗
18. A.E. Hassanien, L.M. Abouelmagd, A.S. Mahmoud et al., An Optimized Backpropagation Neural Network Models for the Prediction of Nanomaterials Concentration for Purification Industrial Wastewater, Eng. Appl. Artif. Intell., 2023, 126, 107010.10.1016/j.engappai.2023.107010 ↗
19. L. Cui, K. Liu, and X.G. Chen, Recent Advances in Cost-Effective Aluminum Alloys with Enhanced Mechanical Performance for High-Temperature Applications: A Review, Mater. Des., 2025 https://doi.org/10.1016/j.matdes.2025.11386910.1016/j.matdes.2025.113869 ↗
20. Y.L. Zhou, Y. Yang, Y.B. Tan et al., Recrystallization Behavior and Texture Evolution During Annealing of Cryogenic-Rolled 3003 Aluminum Alloy, J. Alloys Compd., 2024, 997, 174818.10.1016/j.jallcom.2024.174818 ↗
21. G. Xiao, J. Jiang, W. Ying et al., Microstructure and Mechanical Properties of 7075 Aluminum Alloy Parts Formed by Semi-Solid Thixoextrusion[J], Trans. Nonferrous Metals Soc. China, 2023, 33(11), p 3235–3249.10.1016/S1003-6326(23)66330-7 ↗
22. R.K. Halder, M.N. Uddin, M.A. Uddin et al., Enhancing K-Nearest Neighbor Algorithm: A Comprehensive Review and Performance Analysis of Modifications[J], J. Big Data, 2024, 11(1), p 113.10.1186/s40537-024-00973-y ↗
23. M. Imani, A. Beikmohammadi, and H.R. Arabnia, Comprehensive Analysis of Random Forest and XGBoost Performance with SMOTE, ADASYN, and GNUS Under Varying Imbalance Levels[J], Technologies, 2025, 13(3), p 88.10.3390/technologies13030088 ↗
24. H. Gao, Y. Zhai, and T. Wang, A Deep LSTM-Based Constitutive Model for Describing the Impact Characteristics of Concrete–Granite Composites with Different Roughness Interfaces[J], Sci. Rep., 2024, 14(1), p 29129.10.1038/s41598-024-80366-6 ↗
25. H. Su, D. Zhao, A.A. Heidari et al., RIME: A Physics-Based Optimization[J], Neurocomputing, 2023, 532, p 183–214.10.1016/j.neucom.2023.02.010 ↗
26. W. Huang, W. Li, X. Pan et al., Enhanced Particle Swarm Optimization with Chaotic Search for Offshore Micro-Energy Systems[J], Sci. Rep., 2025, 15(1), p 1191.10.1038/s41598-025-85557-3 ↗
27. T. Chen and C. Guestrin, Xgboost: A Scalable Tree Boosting System[C]. in Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, 785–794 201610.1145/2939672.2939785 ↗
28. D. Chicco, M.J. Warrens, and G. Jurman, The Coefficient of Determination R-Squared is More Informative than SMAPE, MAE, MAPE, MSE and RMSE in Regression Analysis Evaluation, Peerj Compute. Sci., 2021, 7, e623.10.7717/peerj-cs.623 ↗
29. P.N. Suganthan, N. Hansen, J.J. Liang et al., Problem Definitions and Evaluation Criteria for the CEC 2005 Special Session on Real-Parameter Optimization[J], KanGAL Rep., 2005, 2005(2005005), p 2005.
30. S. Nakagawa, P.C.D. Johnson, and H. Schielzeth, The Coefficient of Determination R 2 and Intra-Class Correlation Coefficient From Generalized Linear Mixed-Effects Models Revisited and Expanded[J], J. R. Soc. Interface., 2017, 14(134), p 20170213.10.1098/rsif.2017.0213 ↗
31. J.M. Goedhart, T. Klausch, and M.A. van de Wiel, Estimation of Predictive Performance in High-Dimensional Data Settings Using Learning Curves, Comput. Stat. Data Anal., 2023, 180, 107622.10.1016/j.csda.2022.107622 ↗
32. X. Ren, J. Zhang, Z. Zhang et al., Influence of the Solid Solution Duration on the Microstructure and Mechanical Properties of the Al-10.0 Zn-3.0 Mg-2.5 Cu Alloy, Mater. Sci. Eng. A, 2024, 896, 146299.10.1016/j.msea.2024.146299 ↗
33. J.Z. Gao, Q. Zhu, D.Q. Li et al., Microstructural Changes and Thermal Stability of A201, 319s and 2618 Aluminum Alloys During Thermal Exposure[C]//Materials Science Forum, Trans. Tech. Publ. Ltd, 2018, 913, p 55–62.
34. J. Wang, D. Yi, X. Su et al., Influence of Deformation Ageing Treatment on Microstructure and Properties of Aluminum Alloy 2618[J], Mater Charact, 2008, 59(7), p 965–968.10.1016/j.matchar.2007.08.007 ↗
35. A. Sakthivel, R. Palaninathan, R. Velmurugan et al., The Effect of Silicon Carbide Particulates on Tensile, Fatigue, Impact and Final Fracture Behaviour of 2618 Aluminium Alloy Matrix Composites, Int. J. Aerosp. Innov., 2011 https://doi.org/10.1260/1757-2258.3.3.19310.1260/1757-2258.3.3.193 ↗
36. C. Rockenhäuser, C. Rowolt, B. Milkereit et al., On the Long-Term Aging of S-Phase in Aluminum Alloy 2618A[J], J. Mater. Sci., 2021, 56(14), p 8704–8716.10.1007/s10853-020-05740-x ↗
37. A.R. Farkoosh, D.C. Dunand and D.N. Seidman, Solute-Induced Strengthening During Creep of an Aged-Hardened Al-Mn-Zr Alloy, Acta Mater., 2021, 219, 117268.10.1016/j.actamat.2021.117268 ↗