PSI - Issue 68

ScienceDirect Structural Integrity Procedia 00 (2025) 000–000 Structural Integrity Procedia 00 (2025) 000–000 Available online at www.sciencedirect.com Available online at www.sciencedirect.com ScienceDirect Available online at www.sciencedirect.com ScienceDirect

www.elsevier.com/locate/procedia www.elsevier.com/locate/procedia

Procedia Structural Integrity 68 (2025) 132–138

European Conference on Fracture 2024 Modelling the properties of shape memory alloys using machine learning methods Oleh Yasniy a , Dmytro Tymoshchuk a , Iryna Didych a, *, Volodymyr Iasnii a , Iaroslav Pasternak b a Ternopil Ivan Puluj National Technical University, Ruska str. 56, Ternopil, 46001, Ukraine b Lesya Ukrainka Volyn National University, 13 Voli Ave., Lutsk, 43025, Ukraine Abstract In this paper, the properties of shape memory alloys (SMA), in particular nickel-titanium alloy (Nitinol), were modelled using machine learning methods. The strain of the material e was predicted depending on the applied stress s and the number of loading unloading cycles N by boosted trees, random forests, support vector machines (SVM), k-nearest neighbors (KNN), and artificial neural networks (ANN) algorithms. Experimental data were used to train the models. The highest accuracy was achieved with the ANN, for which the mean absolute percentage error (MAPE) was 0.29% for the loading period and 0.38% for the unloading period. Additional model validation at 127 cycles showed an error of 0.75% for the loading period and 0.92% for the unloading period. These results confirm the high efficiency of ANNs for predicting complex nonlinear material behavior, which can significantly reduce the number of experiments required to study SMA properties. © 2025 The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of ECF24 organizers Keywords: SMA; artificial intelligence; machine learning; random forests; neural networks; boosted trees; SVM; KNN 1. Introduction Shape memory alloys (SMAs) are a class of materials with unique properties that allow them to restore their original shape or structure after exposure to mechanical stress or temperature changes. These characteristics are due to the European Conference on Fracture 2024 Modelling the properties of shape memory alloys using machine learning methods Oleh Yasniy a , Dmytro Tymoshchuk a , Iryna Didych a, *, Volodymyr Iasnii a , Iaroslav Pasternak b a Ternopil Ivan Puluj National Technical University, Ruska str. 56, Ternopil, 46001, Ukraine b Lesya Ukrainka Volyn National University, 13 Voli Ave., Lutsk, 43025, Ukraine Abstract In this paper, the properties of shape memory alloys (SMA), in particular nickel-titanium alloy (Nitinol), were modelled using machine learning methods. The strain of the material e was predicted depending on the applied stress s and the number of loading unloading cycles N by boosted trees, random forests, support vector machines (SVM), k-nearest neighbors (KNN), and artificial neural networks (ANN) algorithms. Experimental data were used to train the models. The highest accuracy was achieved with the ANN, for which the mean absolute percentage error (MAPE) was 0.29% for the loading period and 0.38% for the unloading period. Additional model validation at 127 cycles showed an error of 0.75% for the loading period and 0.92% for the unloading period. These results confirm the high efficiency of ANNs for predicting complex nonlinear material behavior, which can significantly reduce the number of experiments required to study SMA properties. © 2025 The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of ECF24 organizers Keywords: SMA; artificial intelligence; machine learning; random forests; neural networks; boosted trees; SVM; KNN 1. Introduction Shape memory alloys (SMAs) are a class of materials with unique properties that allow them to restore their original shape or structure after exposure to mechanical stress or temperature changes. These characteristics are due to the © 2025 The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of ECF24 organizers

* Corresponding author. Tel.: +380972272074. E-mail address: iryna.didych1101@gmail.com * Corresponding author. Tel.: +380972272074. E-mail address: iryna.didych1101@gmail.com

2452-3216 © 2025 The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of ECF24 organizers 2452-3216 © 2025 The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of ECF24 organizers

2452-3216 © 2025 The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of ECF24 organizers 10.1016/j.prostr.2025.06.033