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Modelling of functional properties of shape-memory alloys by machine learning methods

НазваModelling of functional properties of shape-memory alloys by machine learning methods
Назва англійськоюModelling of functional properties of shape-memory alloys by machine learning methods
АвториOleh Yasniy, Vladyslav Demchyk, Nadiia Lutsyk
ПринадлежністьTernopil Ivan Puluj National Technical University, Ternopil, Ukraine
Бібліографічний описModelling of functional properties of shape-memory alloys by machine learning methods / Oleh Yasniy, Vladyslav Demchyk, Nadiia Lutsyk // Scientific Journal of TNTU. — Tern.: TNTU, 2022. — Vol 108. — No 4. — P. 74–78.
Bibliographic description:Yasniy O., Demchyk V., Lutsyk N. (2022) Modelling of functional properties of shape-memory alloys by machine learning methods. Scientific Journal of TNTU (Tern.), vol 108, no 4, pp. 74–78.
УДК

539.3

Ключові слова

shape-memory alloys, machine learning, regression, k-nearest neighbors, Random Forest, Neural network.

Shape-memory alloys are used in various areas of science and industry due to their unique shape memory effect and superelasticity, caused by martensite and reverse transformations. In this study, it is proposed to model the functional properties of shape memory alloys, namely, the dissipated energy range, strain range and stress range using the methods of machine learning. The modeling is carried ou in the specialized data mining software environment called Orange. There were built five models for each dataset by means of method of neural networks, random forest, gradient boosting, AdaBoost and kNN. The respective regression dependencies are obtained and K fold cross-validation with K=5 is performed. The errors and coefficient for R2 determination are calculated as the results of modeling by means of the above mentioned machine learning methods for the range of dissipated energy, stresses and strains on the number of loading cycles. For each physical quantity, the best results in terms of method error are obtained for k-nearest neighbors method.

ISSN:2522-4433
Перелік літератури
1. Otsuka K. et al. Superelasticity effects and stress-induced martensitic transformations in CuAlNi alloys. Acta Metall. 1976. Vol. 24. No. 3. P. 207–226.
2. Mohd Jani J. et al. A review of shape memory alloy research, applications and opportunities. Mater. Des. Elsevier. 2014. Vol. 56. P. 1078–1113.
3. Zhang X. P., Liu H. Y., Yuan B., & Zhang Y. P. (2008). Superelasticity decay of porous NiTi shape memory alloys under cyclic strain-controlled fatigue conditions. Materials Science and Engineering: A, 481–482 (1–2 C). P. 170–173. URL: https://doi.org/10.1016/J.MSEA.2007.02.147.
4. Petrini L., & Migliavacca F. (2011). Biomedical Applications of Shape Memory Alloys. Journal of Metallurgy. 2011 (Figure 1). P. 1–15. URL: https://doi.org/10.1155/2011/501483.
5. Hartl D. J., & Lagoudas D. C. (2007). Aerospace applications of shape memory alloys. Proceedings of the Institution of Mechanical Engineers. Part G: Journal of Aerospace Engineering, 221 (4). Р. 535–552. URL: https://doi.org/10.1243/09544100JAERO211.
6. Abubakar R. A., Wang F., & Wang L. (2020). A review on Nitinol shape memory alloy heat engines. Smart Materials and Structures. 30 (1). 013001. URL: https://doi.org/10.1088/1361-665X/ABC6B8.
7. Zareie S., Issa A. S., Seethaler R. J. & Zabihollah A. (2020). Recent advances in the applications of shape memory alloys in civil infrastructures: A review. Structures. 27. Р. 1535–1550. URL: https://doi.org/ 10.1016/J.ISTRUC.2020.05.058.
8. Ramprasad R., Batra R., Pilania G., Mannodi-Kanakkithodi A. & Kim C. (2017). Machine learning in materials informatics: Recent applications and prospects. In npj Computational Materials. Vol. 3. Issue 1. URL: https://doi.org/10.1038/s41524-017-0056-5.
9. Bock F. E., Aydin R. C., Cyron C. J., Huber N., Kalidindi S. R. & Klusemann B. (2019). A review of the application of machine learning and data mining approaches in continuum materials mechanics. In Frontiers in Materials. Vol. 6. URL: https://doi.org/10.3389/fmats.2019.00110.
10. Seed G. M. & Murphy G. S. (1998). The applicability of neural networks in modelling the growth of short fatigue cracks. Fatigue & Fracture of Engineering Materials & Structures. 21 (2). Р. 183–190. URL: https:// doi.org/10.1046/J.1460-2695.1998.00329.X.
11. Hu Q., Chen K., Liu F., Zhao M., Liang F. & Xue D. (2022). Smart Materials Prediction: Applying Machine Learning to Lithium Solid-State Electrolyte. Materials. 15 (3). URL: https://doi.org/10.3390/MA15031157.
12. Song Z., Chen X., Meng F., Cheng G., Wang C., Sun Z., Yin W.-J. (2020). Machine learning in materials design: Algorithm and application*. Chinese Physics B, 29 (11), 116103. URL: https://doi.org/10.1088/ 1674-1056/ABC0E3.
13. Mitchell T. M. (2017). Machine Learning. MC GRAW HILL INDIA.
14. Coli G. M., Boattini E., Filion L. & Dijkstra M. (2022). Inverse design of soft materials via a deep learning-based evolutionary strategy. Science Advances. 8 (3). URL: https://doi.org/10.1126/SCIADV.ABJ6731.
15. Trehern W., Ortiz-Ayala R., Atli K. C., Arroyave R. & Karaman, I. (2022). Data-driven shape memory alloy discovery using Artificial Intelligence Materials Selection (AIMS) framework. Acta Materialia, 228, 117751. URL: https://doi.org/10.1016/J.ACTAMAT.2022.117751.
16. Kankanamge U. M. H. U., Reiner J., Ma X., Corujeira Gallo S. & Xu W. (2022). Machine learning guided alloy design of high-temperature NiTiHf shape memory alloys. Journal of Materials Science. 19. URL: https://doi.org/10.1007/s10853-022-07793-6.
17. Sheshadri A. K., Singh S., Botre B. A., Bhargaw H. N., Akbar S. A., Jangid P. & Hasmi S. A. R. (2021). AI models for prediction of displacement and temperature in shape memory alloy (SMA) wire. AIP Conference Proceedings. 2335 (1). 050003. URL: https://doi.org/10.1063/5.0043926.
18. Iasnii V., Yasniy P., Lapusta Yu., Shnitsar T. Experimental study of pseudoelastic NiTi alloy under cyclic loading. Scientific Journal of TNTU. 2018. Vol. 92. No. 4. P. 7–12.
References:
1. Otsuka K. et al. Superelasticity effects and stress-induced martensitic transformations in CuAlNi alloys. Acta Metall. 1976. Vol. 24. No. 3. P. 207–226.
2. Mohd Jani J. et al. A review of shape memory alloy research, applications and opportunities. Mater. Des. Elsevier. 2014. Vol. 56. P. 1078–1113.
3. Zhang X. P., Liu H. Y., Yuan B., & Zhang Y. P. (2008). Superelasticity decay of porous NiTi shape memory alloys under cyclic strain-controlled fatigue conditions. Materials Science and Engineering: A, 481–482 (1–2 C). P. 170–173. URL: https://doi.org/10.1016/J.MSEA.2007.02.147.
4. Petrini L., & Migliavacca F. (2011). Biomedical Applications of Shape Memory Alloys. Journal of Metallurgy. 2011 (Figure 1). P. 1–15. URL: https://doi.org/10.1155/2011/501483.
5. Hartl D. J., & Lagoudas D. C. (2007). Aerospace applications of shape memory alloys. Proceedings of the Institution of Mechanical Engineers. Part G: Journal of Aerospace Engineering, 221 (4). Р. 535–552. URL: https://doi.org/10.1243/09544100JAERO211.
6. Abubakar R. A., Wang F., & Wang L. (2020). A review on Nitinol shape memory alloy heat engines. Smart Materials and Structures. 30 (1). 013001. URL: https://doi.org/10.1088/1361-665X/ABC6B8.
7. Zareie S., Issa A. S., Seethaler R. J. & Zabihollah A. (2020). Recent advances in the applications of shape memory alloys in civil infrastructures: A review. Structures. 27. Р. 1535–1550. URL: https://doi.org/ 10.1016/J.ISTRUC.2020.05.058.
8. Ramprasad R., Batra R., Pilania G., Mannodi-Kanakkithodi A. & Kim C. (2017). Machine learning in materials informatics: Recent applications and prospects. In npj Computational Materials. Vol. 3. Issue 1. URL: https://doi.org/10.1038/s41524-017-0056-5.
9. Bock F. E., Aydin R. C., Cyron C. J., Huber N., Kalidindi S. R. & Klusemann B. (2019). A review of the application of machine learning and data mining approaches in continuum materials mechanics. In Frontiers in Materials. Vol. 6. URL: https://doi.org/10.3389/fmats.2019.00110.
10. Seed G. M. & Murphy G. S. (1998). The applicability of neural networks in modelling the growth of short fatigue cracks. Fatigue & Fracture of Engineering Materials & Structures. 21 (2). Р. 183–190. URL: https:// doi.org/10.1046/J.1460-2695.1998.00329.X.
11. Hu Q., Chen K., Liu F., Zhao M., Liang F. & Xue D. (2022). Smart Materials Prediction: Applying Machine Learning to Lithium Solid-State Electrolyte. Materials. 15 (3). URL: https://doi.org/10.3390/MA15031157.
12. Song Z., Chen X., Meng F., Cheng G., Wang C., Sun Z., Yin W.-J. (2020). Machine learning in materials design: Algorithm and application*. Chinese Physics B, 29 (11), 116103. URL: https://doi.org/10.1088/ 1674-1056/ABC0E3.
13. Mitchell T. M. (2017). Machine Learning. MC GRAW HILL INDIA.
14. Coli G. M., Boattini E., Filion L. & Dijkstra M. (2022). Inverse design of soft materials via a deep learning-based evolutionary strategy. Science Advances. 8 (3). URL: https://doi.org/10.1126/SCIADV.ABJ6731.
15. Trehern W., Ortiz-Ayala R., Atli K. C., Arroyave R. & Karaman, I. (2022). Data-driven shape memory alloy discovery using Artificial Intelligence Materials Selection (AIMS) framework. Acta Materialia, 228, 117751. URL: https://doi.org/10.1016/J.ACTAMAT.2022.117751.
16. Kankanamge U. M. H. U., Reiner J., Ma X., Corujeira Gallo S. & Xu W. (2022). Machine learning guided alloy design of high-temperature NiTiHf shape memory alloys. Journal of Materials Science. 19. URL: https://doi.org/10.1007/s10853-022-07793-6.
17. Sheshadri A. K., Singh S., Botre B. A., Bhargaw H. N., Akbar S. A., Jangid P. & Hasmi S. A. R. (2021). AI models for prediction of displacement and temperature in shape memory alloy (SMA) wire. AIP Conference Proceedings. 2335 (1). 050003. URL: https://doi.org/10.1063/5.0043926.
18. Iasnii V., Yasniy P., Lapusta Yu., Shnitsar T. Experimental study of pseudoelastic NiTi alloy under cyclic loading. Scientific Journal of TNTU. 2018. Vol. 92. No. 4. P. 7–12.
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