بررسی مقایسه‌ای پیش‌بینی فاز در آلیاژهای آنتروپی بالای TiZrNb با رویکردهای یادگیری ماشین

یوسفی, مسعود; رزاقیان آرانی, احمد

doi:10.30501/jamt.2026.569313.1353

بررسی مقایسه‌ای پیش‌بینی فاز در آلیاژهای آنتروپی بالای TiZrNb با رویکردهای یادگیری ماشین

نوع مقاله : مقاله کامل پژوهشی

نویسندگان

مسعود یوسفی ¹

احمد رزاقیان آرانی ²

¹ دانشجوی پسادکتری مهندسی مواد، گروه مهندسی و علم مواد، دانشکده‌ی فنی و مهندسی، دانشگاه بین‌المللی امام خمینی)ره(، قزوین، ایران

² استاد، گروه مهندسی و علم مواد، دانشکده‌ی فنی و مهندسی، دانشگاه بین‌المللی امام خمینی(ره)، قزوین، ایران

10.30501/jamt.2026.569313.1353

چکیده

یکی از چالش‌های اصلی در توسعهی آلیاژهای آنتروپی بالا، به‌ویژه انواع زیست‌سازگار، هزینهی بالای آزمایش‌های تجربی است. در این پژوهش، به‌منظور کاهش تعداد آزمایش‌ها و تسریع در طراحی آلیاژهای جدید، از روش‌های یادگیری ماشین برای پیش‌بینی فازهای تشکیل‌شده (محلول جامد SS، فاز بین‌فلزی IM و ترکیب SS+IM) استفاده شد. پایگاه داده‌ای شامل حدود 400 آلیاژ آنتروپی بالا به‌عنوان مرجع انتخاب شد و پارامترهای ترمودینامیکی کلیدی نظیر ΔHₘᵢₓ، ΔSₘᵢₓ، δ،VEC و Δχ ویژگی‌های ورودی در نظر گرفته شدند. دو رویکرد اصلی بررسی شدند: الگوریتم جنگل تصادفی (Random Forest) و الگوریتم‌های یادگیری گروهی بگینگ (Bagging) و بوستینگ (Boosting). نتایج نشان داد که دقت طبقه‌بندی سه‌فازی با روش جنگل تصادفی و روش بگینگ به‌طور متوسط به بیش از ۷۵ درصد رسید. این مطالعه نشان می‌دهد که الگوریتم‌های جنگل تصادفی و یادگیری گروهی ابزارهای مؤثر و کم‌هزینه‌ای برای پیش‌بینی فاز در آلیاژهای آنتروپی بالا هستند و می‌توانند به‌طور چشمگیری فرایند طراحی آلیاژهای جدید به‌ویژه برای کاربردهای زیست‌پزشکی و ایمپلنت‌های ارتوپدی را تسریع کنند.

کلیدواژه‌ها

یادگیری ماشین

TiZrNb

آلیاژ آنتروپی بالا

پیش‌بینی فاز

موضوعات

دیگر (مدلسازی، شبیه سازی، طراحی آزمایش و ...)

عنوان مقاله English

Comparative Analysis of Phase Prediction in High-Entropy TiZrNb Alloys Using Machine Learning Approaches

نویسندگان English

Masoud Yousefi ¹

Ahmad Razaghian Arani ²

¹ Postdoctoral Researcher in Materials Engineering, Department of Materials Science and Engineering, Faculty of Technical and Engineering, Imam Khomeini International University (IKIU), Qazvin, Iran.

² Professor, Department of Materials Science and Engineering, Faculty of Technical and Engineering, Imam Khomeini International University (IKIU), Qazvin, Iran.

چکیده English

A primary challenge in the development of high-entropy alloys (HEAs), particularly biocompatible variants, is the substantial cost associated with experimental trial-and-error procedures. In this study, machine learning (ML) models were employed to predict phase formation—specifically, solid solution (SS), intermetallic (IM), and composite (SS+IM) phases—to minimize experimental overhead and expedite the alloy design process. A reference database comprising approximately 400 HEAs was utilized, with key thermodynamic parameters—namely ΔHmix, ΔSmix, δ, VEC, and Δχ—serving as input features. Two primary learning frameworks were evaluated: the Random Forest (RF) algorithm and ensemble learning techniques, specifically Bagging and Boosting. The results indicate that the three-phase classification accuracy reached over 75% on average using the Random Forest and Bagging methods. This study demonstrates that these ensemble algorithms constitute effective, cost-efficient computational tools for phase prediction in HEAs, significantly accelerating the design of novel alloys tailored for biomedical applications and orthopedic implants.

کلیدواژه‌ها English

Machine Learning

TiZrNb

High-Entropy Alloy

Phase prediction

1. Banfield, R. E., Hall, L. O., Bowyer, K. W., & Kegelmeyer, W. P. (2007). A comparison of decision tree ensemble creation techniques. IEEE Trans Pattern Anal Mach Intell., 29(1), 173-80. https://doi.org/10.1109/tpami.2007.250609. PMID: 17108393

2. Breiman, L. (2001). Random forests. Machine Learning, 45, 5–32. https://doi.org/10.1023/A:1010933404324

3. Cantor, B. (2014). Multicomponent and high entropy alloys. Entropy, 16(9), 4749–4768. https://doi.org/10.3390/e16094749

4. Feng, R., Liaw, P. K., Gao, M. C. et al. (2017). First-principles prediction of high-entropy-alloy stability. Npj Comput Mater, 3, 50. https://doi.org/10.1038/s41524-017-0049-4

5. Fontana, M. G., & Greene, N. D. (2018). Corrosion engineering. McGraw-Hill. https://repository.um-palembang.ac.id/id/eprint/9139/1/Mars%20Fontana-Corrosion%20Engineering%28www.iranidata.com%29.pdf

6. Haase, C., Tang, F., Wilms, M. B., Weisheit, A., & Hallstedt, B. (2017). Combining thermodynamic modeling and 3D printing of elemental powder blends for high-throughput investigation of high-entropy alloys – Towards rapid alloy screening and desig. Materials Science and Engineering: A, 688, 180-189, ISSN 0921-5093. https://doi.org/10.1016/j.msea.2017.01.099

7. Han, L., Luo, S., Yu, J., Pan, L., & Chen, S. (2015). Rule extraction from support vector machines using ensemble learning approach: an application for diagnosis of diabetes. IEEE J Biomed Health Inform, 19(2), 728-734. https://doi.org/10.1109/JBHI.2014.2325615. Epub 2014 May 19. PMID: 24860043.

8. Harrysson, O. L. A., & Cormier, D. R. (2005). Direct fabrication of custom orthopedic implants using electron beam melting technology. In Advanced Manufacturing Technology for Medical Applications (pp. 191–206). https://scispace.com/pdf/direct-fabrication-of-custom-orthopedic-implants-using-2mtipatlwj.pdf

9. Ho, T. K. (1998). The Random Subspace Method for Constructing Decision Forests. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20, 832-844. http://dx.doi.org/10.1109/34.709601

10. Hu, J., Zhang, J., Xiao, H., Xie, L., Shen, H., Li, P., ... & Zu, X. (2020). A density functional theory study of the hydrogen absorption in high entropy alloy TiZrHfMoNb. Inorganic Chemistry, 59(14), 9774-9782. https://doi.org/10.1021/acs.inorgchem.0c00989

11. Huang, W., Martin, P., & Zhuang, H. L. (2019). Machine-learning phase prediction of high-entropy alloys. Acta Materialia, 169, 225–236. https://doi.org/10.1016/j.actamat.2019.03.012

12. Huhn, W. P., & Widom, M. (2013). Prediction of A2 to B2 phase transition in the high-entropy alloy Mo-Nb-Ta-W. JOM, 65, 1772–1779. https://doi.org/10.1007/s11837-013-0772-3

13. King, D. J. M., Middleburgh, S. C., McGregor, A. G., & Cortie, M. B. (2016). Predicting the formation and stability of single phase high-entropy alloys. Acta Materialia, 104, 172-179. ISSN 1359-6454. https://doi.org/10.1016/j.actamat.2015.11.040

14. Krishna, Y. V., Jaiswal, U. K., & Rahul, M. R. (2021). Machine learning approach to predict new multiphase high entropy alloys. Scripta Materialia, 197, 113804. https://doi.org/10.1016/j.scriptamat.2021.113804

15. Lee, S. Y., Byeon, S., Kim, H. S., Jin, H., & Lee, S. (2021). Deep learning-based phase prediction of high-entropy alloys: Optimization, generation, and explanation. Materials & Design, 197, 109260. https://doi.org/10.1016/j.matdes.2020.109260

16. Leon, F., Floria, S. A., & Bădică, C. (2017). Evaluating the effect of voting methods on ensemble-based classification. In 2017 IEEE International Symposium on Intelligent Signal Processing (INISTA). https://10.1109/INISTA.2017.8001122

17. Lind, A. P., & Anderson, P. C. (2019). Predicting drug activity against cancer cells by random forest models based on minimal genomic information and chemical properties. PLOS ONE, 14(7), e0219774. https://doi.org/10.1371/journal.pone.0219774

18. Liu, Y., Wang, Y., & Zhang, J. (2012). New machine learning algorithm: Random Forest. In ICICA 2012. Springer. https://doi.org/10.1007/978-3-642-34062-8_32

19. Machaka, R. (2021). Machine learning-based prediction of phases in high-entropy alloys. Computational Materials Science, 188, 110244. https://doi.org/10.1016/j.commatsci.2020.110244

20. Miracle, D. B., & Senkov, O. N. (2017). A critical review of high entropy alloys and related concepts. Acta Materialia, 122, 448–511. https://doi.org/10.1016/j.actamat.2016.08.081

21. Qiao, L., Liu, Y., & Zhu, J. (2021). A focused review on machine learning aided high-throughput methods in high entropy alloy. Journal of Alloys and Compounds, 877, 160295. https://doi.org/10.1016/j.jallcom.2021.160295

22. Rickman, J. M., Balasubramanian, G., Marvel, C. J., Chan, H. M., & Burton, M. T. (2020). Machine learning strategies for high-entropy alloys. Journal of Applied Physics, 128(22). https://doi.org/10.1063/5.0030367

23. Roy, A., & Balasubramanian, G. (2021). Predictive descriptors in machine learning and data-enabled explorations of high-entropy alloys. Computational Materials Science, 193, 110381. https://doi.org/10.1016/j.commatsci.2021.110381

24. Ruta, D., Gabrys, B., & Lemke, C. (2010). A generic multilevel architecture for time series prediction. IEEE Transactions on Knowledge and Data Engineering, 23(3), 350–359. https://doi.org/10.1109/TKDE.2010.137

25. Schapire, R. E., & Singer, Y. (1998). Improved boosting algorithms using confidence-rated predictions. In Proceedings of the 11th Annual Conference on Computational Learning Theory (COLT). https://doi.org/10.1145/279943.279960

26. Singh, S. K., & Singh, V. K. (2021). Design and development of high entropy alloys using artificial intelligence. IntechOpen. https://doi.org/10.5772/intechopen.96761

27. Smallman, R. E., & Ngan, A. H. W. (2011). Physical metallurgy and advanced materials. Elsevier. https://shop.elsevier.com/books/physical-metallurgy-and-advanced-materials/smallman/978-0-7506-6906-1

28. Sun, W., Huang, X., & Luo, A. A. (2017). Phase formations in low density high entropy alloys. Calphad, 56, 19–28. https://doi.org/10.1016/j.calphad.2016.11.002

29. Talebi, H., Peeters, L. J., Otto, A., & Tolosana-Delgado, R. (2022). A truly spatial random forests algorithm for geoscience data analysis and modelling. Mathematical Geosciences, 54(1), 1-22. https://doi.org/10.1007/s11004-021-09946-w

30. Tokar, O., Vovk, O., Kolyasa, L., Havryliuk, S., & Korol, M. (2018, September). Using the Random Forest Classification for Land Cover Interpretation of Landsat Images in the Prykarpattya Region of Ukraine. In CSIT (1) (pp. 241-244). https://doi.org/10.1109/STC-CSIT.2018.8526646

31. Tsai, M. H., & Yeh, J. W. (2014). High-entropy alloys: A critical review. Materials Research Letters, 2(3), 107–123. https://doi.org/10.1080/21663831.2014.912690

32. Vaidya, M., Muralikrishna, G. M., & Murty, B. S. (2019). High-entropy alloys by mechanical alloying: A review. Journal of Materials Research, 34(5), 664–686. https://doi.org/10.1557/jmr.2019.37

33. Ward, L., Agrawal, A., Choudhary, A., & Wolverton, C. (2016). A general-purpose machine learning framework for predicting properties of inorganic materials. npj Computational Materials, 2(1), 16028. https://doi.org/10.1038/npjcompumats.2016.28

34. Yang, B. S., Di, X., & Han, T. (2008). Random forests classifier for machine fault diagnosis. Journal of Mechanical Science and Technology, 22, 1716–1725. https://doi.org/10.1007/s12206-008-0603-6

35. Ye, Y. F., Wang, Q., Lu, J., Liu, C. T., & Yang, Y. J. M. T. (2016). High-entropy alloy: challenges and prospects. Materials Today, 19(6), 349-362. https://doi.org/10.1016/j.mattod.2015.11.026

36. Yeh, J. W. (2016). Overview of high-entropy alloys. In High-entropy alloys: Fundamentals and applications (pp. 1–19). doi: https://doi.org/10.1007/978-3-319-27013-5

37. Yousefi, M., Rajabi, M., Reyhani, A., & Asgari, N. (2024a). Corrosion and biocompatibility properties of TiZrNbCrV, TiZrNbFeCr, and TiZrNbFeV. Journal of Materials Engineering and Performance, 33. https://doi.org/10.1007/s11665-023-08990-3

38. Yousefi, M., Rahmani, K., Rajabi, M., Reyhani, A., & Moudi, M. (2024b). Random forest classifier for high entropy alloys phase diagnosis. Afrika Matematika, 35(3), 57. https://doi.org/10.1007/s13370-024-01198-1

39. Yousefi, M., Rahmani, K., & Rajabi, M., (2024c). Investigating X-ray diffraction and microstructure analysis of new high entropy alloys TiZrNbXX. Advanced Processes in Materials Engineering, 18, 69. https://jnm.marvdasht.iau.ir/?lang=fa

40. Yousefi, M., Rajabi, M., Reyhani, A., Asgari, N., & Rahmani, K. (2025a). Investigation of Microstructure of Pressed and Sintered TiZrNbCrV, TiZrNbFeCr, and TiZrNbFeV High Entropy Alloys Produced by Powder Metallurgy and Mechanical Alloying Methods. Metal Science and Heat Treatment, 1-7. https://doi.org/10.1007/s11041-025-01165-y

41. Yousefi, M., Rahmani, K., Rajabi, M., Reyhani, A., & Asgari, N. (2025b). The ensemble learning algorithms for prediction high entropy alloys phases. Afrika Matematika, 36(3), 117. https://doi.org/10.1007/s13370-025-01334-5

42. Zhang, Y., Zhou, Y. J., Lin, J. P., Chen, G. L., & Liaw, P. K. (2008). Solid‐solution phase formation rules for multi‐component alloys. Advanced engineering materials, 10(6), 534-538. https://doi.org/10.1002/adem.200700240

43. Zhang, Y. (2010). Mechanical properties and structures of high entropy alloys and bulk metallic glasses composites. In Materials Science Forum (Vol. 654). https://doi.org/10.4028/www.scientific.net/MSF.654-656.1058

44. Zhang, C. H., Lin, M. H., Wu, B., Ye, G. X., Zhang, L. K., Chen, T., ... & Wang, C. (2011). Explore the possibility of forming fcc high entropy alloys in equal-atomic systems CoFeMnNi M and CoFeMnNiSm M. Journal of Shanghai Jiaotong University (Science), 16(2), 173-179. https://doi.org/10.1007/s12204-011-1113-8

45. Zhang, B., Qi, S., Monkam, P., Li, C., Yang, F., Yao, Y. D., & Qian, W. (2019). Ensemble learners of multiple deep CNNs for pulmonary nodules classification using CT images. IEEE Access, 7, 110358-110371. https://doi.org/10.1109/ACCESS.2019.2933670