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Modelling of functional properties of shape memory alloy by machine learning methods

НазваModelling of functional properties of shape memory alloy by machine learning methods
Назва англійськоюModelling of functional properties of shape memory alloy by machine learning methods
АвториVladyslav Demchyk, Oleh Yasniy
ПринадлежністьTernopil Ivan Puluj National Technical University, Ternopil, Ukraine
Бібліографічний описModelling of functional properties of shape memory alloy by machine learning methods / Vladyslav Demchyk, Oleh Yasniy // Scientific Journal of TNTU. — Tern.: TNTU, 2025. — Vol 119. — No 3. — P. 56–62.
Bibliographic description:Demchyk V., Yasniy O. (2025) Modelling of functional properties of shape memory alloy by machine learning methods. Scientific Journal of TNTU (Tern.), vol 119, no 3, pp. 56–62.
DOI: https://doi.org/10.33108/visnyk_tntu2025.03.056
УДК

004.8; 620.1

Ключові слова

shape memory alloy, machine learning, dissipated energy, random forest, AdaBoost, decision trees, data science, data analytics; data mining, big data, regression problem.

This study deals with the modelling of NiTi shape memory alloy dissipated energy by means of supervised machine learning methods, considering the loading frequency. Shape memory alloys are materials of high interest both to science and industry. These materials are enjoying wide popularity due to their two peculiar properties: unique effect of shape memory and superplasticity, caused by direct austenite-martensite phase transformation and reverse martensite-austenite transformation. The traditional deterministic methods of assessment of material properties are often costly, time-consuming, and demand a well-trained workforce and laboratory equipment. On the contrary, in recent years, the methods of artificial intelligence have gained widespread attention due to their ability to reveal hidden insights from existing data. Machine learning is a subset of artificial intelligence. It allows training based on the available data and becomes better with time without the explicit need to be programmed. The experimental dataset was taken from open scientific sources. It contained the hysteresis curves for six loading frequencies of 0.1, 0.5, 1, 5, 7, and 10 Hz. The input data consisted of the next features: stress s (MPa), cycle number N, and loading frequency f (Hz). Based on these data, for each loading cycle, and for each loading cycle, the dissipated energy was calculated. To remove noise, Locally Weighted Scatterplot Smoothing (LOWESS) smoother in the nonparametric package of statsmodels was utilized. After that, the trapezoid numerical integration method was employed to calculate the area enclosed by the hysteresis loop of the respective cycle, that is, the dissipated energy. To augment the dataset, its points were interpolated using the modified Akima interpolation method (makima). Four models were built using the methods of Random Forest, AdaBoost, Gradient Boosting, and Neural Network. The best results were shown by the ensemble methods, such as AdaBoost, and Random Forest. For instance, the MAPE of AdaBoost was just 0.074, whereas the MAPE of Random Forest was 0.144. It was found that the Gradient Boosting method and Neural Network are not suitable for such a dataset, since the errors are quite large and, therefore, these methods are not good enough to be employed for solving such a problem.

Перелік літератури
  1. Lagoudas D. C. (Ed.). (2008). Shape Memory Alloys: Modeling and Engineering Applications. Springer. https://doi.org/10.1007/978-0-387-47685-8.
  2. Ghosh P., Rao A., Srinivasa A. R. (2013) Design of multi-state and smart-bias components using shape memory alloy and shape memory polymer composites. Materials & Design, 44, pp. 164–171. https://doi.org/10.1016/j.matdes.2012.05.063.
  3. Momoda L. A. (2004) The future of engineering materials: multifunction for performance tailored structures. The Bridge, 34 (3), pp. 13–18.
  4. Rao A., Srinivasa A. R., Reddy J. N. Design of shape memory alloy (SMA) actuators. Springer. https://doi.org/10.1007/978-3-319-03188-0.
  5. Safa H. Mohammed, Sara H. Shahatha Shape memory alloys, properties and applications: A review. AIP Conf. Proc. 22 May 2023; 2593 (1): 020008. https://doi.org/10.1063/5.0112999.
  6. Otsuka K., Ren X. (1999) Recent developments in the research of shape memory alloys, Intermetallics, 7 (5), pp. 511–528. https://doi.org/10.1016/S0966-9795(98)00070-3.
  7. Hmede R., Chapelle F., Lapusta Y. (2022) Review of Neural Network Modeling of Shape Memory Alloys, Sensors, 22 (15), 5610. https://doi.org/10.3390/s22155610.
  8. Jani J. M., J., Leary M., Subic A., & Gibson M. A. (2014) A review of shape memory alloy research, applications and opportunities. Materials and Design, 56, pp. 1078–1113. https://doi.org/10.1016/j.matdes.2013.11.084.
  9. W. Huang (2002) On the selection of shape memory alloys for actuators, Materials & Design, 23 (1), pp. 11–19, ISSN 0261-3069, https://doi.org/10.1016/S0261-3069(01)00039-5.
  10. Fink A., Fu Z, Körner C. (2023) Functional properties and shape memory effect of Nitinol manufactured via electron beam powder bed fusion, Materialia, 30, 101823, ISSN 2589–1529, https://doi.org/10.1016/j.mtla.2023.101823.
  11. Elahinia M., Moghaddam N. S., Andani M. T.,  Amerinatanzi A., Bimber B. A., Hamilton R. F. (016) Fabrication of NiTi through additive manufacturing: A review, Progress in Materials Science, 83, pp. 630–663, ISSN 0079-6425, https://doi.org/10.1016/j.pmatsci.2016.08.001.
  12. Song G., Ma N., Li H.-N. (2006) Applications of shape memory alloys in civil structures, Engineering Structures, 28 (9), pp. 1266–1274, ISSN 0141-0296. https://doi.org/10.1016/j.engstruct.2005.12.010.
  13. Zhang A., Lipton Z. C., Li M., Smola A. J. Dive into Deep Learning, 2023, Cambridge University Press, 2023.
  14. James G., Witten D., Hastie T., Tibshirani R., Taylor J. (2023). An Introduction to Statistical Learning: With Applications in Python. Springer.
  15. Yasniy O., Demchyk V., Lutsyk N. (2022) Modelling of functional properties of shape-memmory alloys by machine learning methods. Scientific Journal of the Ternopil National Technical University, 108, pp. 74–78. https://doi.org/10.33108/visnyk_tntu2022.04.074.
  16. Yasniy O., Demchyk V. (2025) Shape memory alloys and machine Learning: a review. Measuring and computing devices in technological processes, 82, pp. 13–17. https://doi.org/10.31891/2219-9365-2025-82-2.
  17. Yasniy O., Tymoshchuk D., Didych I., Iasnii V., Pasternak Ia. (2025) Modelling the properties of shape memory alloys using machine learning methods. Procedia Structural Integrity, 68, pp. 132–138. https://doi.org/10.1016/j.prostr.2025.06.033.
  18. Tymoshchuk D., Yasniy O., Maruschak P., Iasnii V., Didych I. (2024) Loading Frequency Classification in Shape Memory Alloys: A Machine Learning Approach. Computers, 13 (12), 339. https://doi.org/10.3390/computers13120339.
  19. Iasnii V., Bykiv N., Yasniy O., Budz V. (2022) Methodology and some results of studying the influence of frequency on functional properties of pseudoelastic SMA. Scientific Journal of the Ternopil National Technical University, 107 (3), pp. 45–50 https://doi.org/10.33108/visnyk_tntu2022.03.045. 
  20. William S. (1979) Cleveland. Robust Locally Weighted Regression and Smoothing Scatterplots, Journal of the American Statistical Association, 74, 368, pp. 829–836.
  21. Makima Piecewise Cubic Interpolation. Cleve Moler and Cosmin Ionita, 2019. https://blogs.mathworks. com/cleve/2019/04/29/makima-piecewise-cubic-interpolation/.
References:
  1. Lagoudas D. C. (Ed.). (2008). Shape Memory Alloys: Modeling and Engineering Applications. Springer. https://doi.org/10.1007/978-0-387-47685-8.
  2. Ghosh P., Rao A., Srinivasa A. R. (2013) Design of multi-state and smart-bias components using shape memory alloy and shape memory polymer composites. Materials & Design, 44, pp. 164–171. https://doi.org/10.1016/j.matdes.2012.05.063.
  3. Momoda L. A. (2004) The future of engineering materials: multifunction for performance tailored structures. The Bridge, 34 (3), pp. 13–18.
  4. Rao A., Srinivasa A. R., Reddy J. N. Design of shape memory alloy (SMA) actuators. Springer. https://doi.org/10.1007/978-3-319-03188-0.
  5. Safa H. Mohammed, Sara H. Shahatha Shape memory alloys, properties and applications: A review. AIP Conf. Proc. 22 May 2023; 2593 (1): 020008. https://doi.org/10.1063/5.0112999.
  6. Otsuka K., Ren X. (1999) Recent developments in the research of shape memory alloys, Intermetallics, 7 (5), pp. 511–528. https://doi.org/10.1016/S0966-9795(98)00070-3.
  7. Hmede R., Chapelle F., Lapusta Y. (2022) Review of Neural Network Modeling of Shape Memory Alloys, Sensors, 22 (15), 5610. https://doi.org/10.3390/s22155610.
  8. Jani J. M., J., Leary M., Subic A., & Gibson M. A. (2014) A review of shape memory alloy research, applications and opportunities. Materials and Design, 56, pp. 1078–1113. https://doi.org/10.1016/j.matdes.2013.11.084.
  9. W. Huang (2002) On the selection of shape memory alloys for actuators, Materials & Design, 23 (1), pp. 11–19, ISSN 0261-3069, https://doi.org/10.1016/S0261-3069(01)00039-5.
  10. Fink A., Fu Z, Körner C. (2023) Functional properties and shape memory effect of Nitinol manufactured via electron beam powder bed fusion, Materialia, 30, 101823, ISSN 2589–1529, https://doi.org/10.1016/j.mtla.2023.101823.
  11. Elahinia M., Moghaddam N. S., Andani M. T.,  Amerinatanzi A., Bimber B. A., Hamilton R. F. (016) Fabrication of NiTi through additive manufacturing: A review, Progress in Materials Science, 83, pp. 630–663, ISSN 0079-6425, https://doi.org/10.1016/j.pmatsci.2016.08.001.
  12. Song G., Ma N., Li H.-N. (2006) Applications of shape memory alloys in civil structures, Engineering Structures, 28 (9), pp. 1266–1274, ISSN 0141-0296. https://doi.org/10.1016/j.engstruct.2005.12.010.
  13. Zhang A., Lipton Z. C., Li M., Smola A. J. Dive into Deep Learning, 2023, Cambridge University Press, 2023.
  14. James G., Witten D., Hastie T., Tibshirani R., Taylor J. (2023). An Introduction to Statistical Learning: With Applications in Python. Springer.
  15. Yasniy O., Demchyk V., Lutsyk N. (2022) Modelling of functional properties of shape-memmory alloys by machine learning methods. Scientific Journal of the Ternopil National Technical University, 108, pp. 74–78. https://doi.org/10.33108/visnyk_tntu2022.04.074.
  16. Yasniy O., Demchyk V. (2025) Shape memory alloys and machine Learning: a review. Measuring and computing devices in technological processes, 82, pp. 13–17. https://doi.org/10.31891/2219-9365-2025-82-2.
  17. Yasniy O., Tymoshchuk D., Didych I., Iasnii V., Pasternak Ia. (2025) Modelling the properties of shape memory alloys using machine learning methods. Procedia Structural Integrity, 68, pp. 132–138. https://doi.org/10.1016/j.prostr.2025.06.033.
  18. Tymoshchuk D., Yasniy O., Maruschak P., Iasnii V., Didych I. (2024) Loading Frequency Classification in Shape Memory Alloys: A Machine Learning Approach. Computers, 13 (12), 339. https://doi.org/10.3390/computers13120339.
  19. Iasnii V., Bykiv N., Yasniy O., Budz V. (2022) Methodology and some results of studying the influence of frequency on functional properties of pseudoelastic SMA. Scientific Journal of the Ternopil National Technical University, 107 (3), pp. 45–50 https://doi.org/10.33108/visnyk_tntu2022.03.045. 
  20. William S. (1979) Cleveland. Robust Locally Weighted Regression and Smoothing Scatterplots, Journal of the American Statistical Association, 74, 368, pp. 829–836.
  21. Makima Piecewise Cubic Interpolation. Cleve Moler and Cosmin Ionita, 2019. https://blogs.mathworks. com/cleve/2019/04/29/makima-piecewise-cubic-interpolation/.
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