Veuillez utiliser cette adresse pour citer ce document : http://elartu.tntu.edu.ua/handle/lib/47589
Titel: Modelling of automotive steel fatigue lifetime by machine learning method
Författare: Yasniy, Oleh
Tymoshchuk, Dmytro
Didych, Iryna
Zagorodna, Nataliya
Malyshevska, Olha
Affiliation: Ternopil Ivan Puluj National Technical University, Ruska str. 56, Ternopil, 46001, Ukraine
Bibliographic description (Ukraine): Yasniy, O., Tymoshchuk, D., Didych, I., Zagorodna, N., Malyshevska O., (2024). Modelling of automotive steel fatigue lifetime by machine learning method. CEUR Workshop Proceedings, 3896, pp. 165-172.
Bibliographic citation (APA): Yasniy, O., Tymoshchuk, D., Didych, I., Zagorodna, N., Malyshevska O. (2024). Modelling of automotive steel fatigue lifetime by machine learning method. CEUR Workshop Proceedings, 3896, 165-172.
Conference/Event: 4th International Workshop on Information Technologies: Theoretical and Applied Problems, ITTAP 2024
Volume: 3896
Utgivningsdatum: 23-okt-2024
Date of entry: 16-jan-2025
Utgivare: CEUR Workshop Proceedings
Place of the edition/event: Ternopil, Ukraine, Opole, Poland, October 23-25, 2024.
Nyckelord: machine learning
neural network
fatigue life
crack length
QSTE340TM steel
Page range: 165-172
Start page: 165
End page: 172
Sammanfattning: In the current study, the fatigue life of QSTE340TM steel was modelled using a machine learning method, namely, a neural network. This problem was solved by a Multi-Layer Perceptron (MLP) neural network with a 3-75-1 architecture, which allows the prediction of the crack length based on the number of load cycles N, the stress ratio R, and the overload ratio Rol. The proposed model showed high accuracy, with mean absolute percentage error (MAPE) ranging from 0.02% to 4.59% for different R and Rol. The neural network effectively reveals the nonlinear relationships between input parameters and fatigue crack growth, providing reliable predictions for different loading conditions.
URI: http://elartu.tntu.edu.ua/handle/lib/47589
ISSN: 1613-0073
Copyright owner: © Oleh Yasniy, Dmytro Tymoshchuk, Iryna Didych, Nataliya Zagorodna, Olha Malyshevska
References (Ukraine): [1] Salzgitter Flachstahl GmbH, QStE340TM, Accessed: Aug. 21, 2024. URL: https://www.salzgitter-flachstahl.de/fileadmin/mediadb/szfg/ informationsmaterial/produktinformationen/warmgewalzte_produkte/eng/QStE340TM.pdf.
[2] Yasniy O., Pasternak I., Didych I., Fedak S., Tymoshchuk D. Methods of jump-like creep modeling of AMg6 aluminum alloy. Procedia Structural Integrity, 2023, 48: 149–154. doi:10.1016/j.prostr.2023.07.141.
[3] Didych, I., Yasniy, O., Fedak, S., Lapusta, Y. Prediction of jump-like creep using preliminary plastic strain. Procedia Structural Integrity, 2022, 36, 166-170. doi:10.1016/j.prostr.2022.01.019.
[4] Tymoshchuk, D., Yasniy, O., Maruschak, P., Iasnii, V., Didych, I. Loading Frequency Classification in Shape Memory Alloys: A Machine Learning Approach. Computers, 2024. 13(12), 339. doi: 10.3390/computers13120339.
[5] Okipnyi, I.B., Maruschak, P.O., Zakiev, V.I. et al. Fracture Mechanism Analysis of the Heat-Resistant Steel 15Kh2MFA(II) After Laser Shock-Wave Processing. J Fail. Anal. and Preven. 14, 668–674 (2014). doi:10.1007/s11668-014-9869-4.
[6] I. Konovalenko, P. Maruschak, J. Brezinová, O. Prentkovskis, J. Brezina, Research of u-net-based CNN architectures for metal surface defect detection, Machines 10.5 (2022) 327. doi:10.3390/machines10050327.
[7] Y. Lu, F. Yang, T. Chen. Effect of single overload on fatigue crack growth in QSTE340TM steel and retardation model modification. Engineering Fracture Mechanics, 2019, 212: 81-94. doi:10.1016/j.engfracmech.2019.03.029.
[8] Y. Lu, F. Yang, T. Chen. Data for: Effect of single overload on fatigue crack growth in QSTE340TM steel and retardation model modification. Engineering Fracture Mechanics, 2019, 212: 81-94. doi:10.17632/8pcx2mgfd4.1.
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Content type: Article
Samling:Наукові публікації працівників кафедри кібербезпеки

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