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http://elartu.tntu.edu.ua/handle/lib/47589Full metadata record
| DC pole | Hodnota | Jazyk |
|---|---|---|
| dc.contributor.author | Yasniy, Oleh | - |
| dc.contributor.author | Tymoshchuk, Dmytro | - |
| dc.contributor.author | Didych, Iryna | - |
| dc.contributor.author | Zagorodna, Nataliya | - |
| dc.contributor.author | Malyshevska, Olha | - |
| dc.date.accessioned | 2025-01-16T21:30:55Z | - |
| dc.date.available | 2025-01-16T21:30:55Z | - |
| dc.date.issued | 2024-10-23 | - |
| dc.identifier.citation | 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. | uk_UA |
| dc.identifier.issn | 1613-0073 | - |
| dc.identifier.uri | http://elartu.tntu.edu.ua/handle/lib/47589 | - |
| dc.description.abstract | 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. | uk_UA |
| dc.format.extent | 165-172 | - |
| dc.publisher | CEUR Workshop Proceedings | uk_UA |
| dc.subject | machine learning | uk_UA |
| dc.subject | neural network | uk_UA |
| dc.subject | fatigue life | uk_UA |
| dc.subject | crack length | uk_UA |
| dc.subject | QSTE340TM steel | uk_UA |
| dc.title | Modelling of automotive steel fatigue lifetime by machine learning method | uk_UA |
| dc.type | Article | uk_UA |
| dc.rights.holder | © Oleh Yasniy, Dmytro Tymoshchuk, Iryna Didych, Nataliya Zagorodna, Olha Malyshevska | uk_UA |
| dc.coverage.placename | Ternopil, Ukraine, Opole, Poland, October 23-25, 2024. | uk_UA |
| dc.relation.references | [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. | uk_UA |
| dc.relation.references | [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. | uk_UA |
| dc.relation.references | [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. | uk_UA |
| dc.relation.references | [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. | uk_UA |
| dc.relation.references | [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. | uk_UA |
| dc.relation.references | [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. | uk_UA |
| dc.relation.references | [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. | uk_UA |
| dc.relation.references | [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. | uk_UA |
| dc.relation.references | [9] Rosenblatt, F. Principles of neurodynamics. perceptrons and the theory of brain mechanisms. Cornell Aeronautical Lab Inc Buffalo NY, 1961. | uk_UA |
| dc.contributor.affiliation | Ternopil Ivan Puluj National Technical University, Ruska str. 56, Ternopil, 46001, Ukraine | uk_UA |
| dc.citation.volume | 3896 | - |
| dc.citation.spage | 165 | - |
| dc.citation.epage | 172 | - |
| dc.citation.conference | 4th International Workshop on Information Technologies: Theoretical and Applied Problems, ITTAP 2024 | - |
| dc.identifier.citationenAPA | 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. | uk_UA |
| Vyskytuje se v kolekcích: | Наукові публікації працівників кафедри кібербезпеки | |
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| Soubor | Popis | Velikost | Formát | |
|---|---|---|---|---|
| ITTAP_2024_3896_short4_tntu.pdf | 1,01 MB | Adobe PDF | Zobrazit/otevřít |
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