PSI - Issue 68

Tea Marohnić et al. / Procedia Structural Integrity 68 (2025) 84 – 90 T. Marohnić and R. Basan / Structural Integrity Procedia 00 (2025) 000–000

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Comparability is a must no matter the tool used, so the methodology for evaluation of ML-based methods for estimation of strain-life parameters and curves should be the same as for empirical estimation methods. Acknowledgements This research has been supported by Croatian Science Foundation under the project IP-2020-02-5764 and by the University of Rijeka under the projects number uniri-tehnic-18-116 and uniri-iskusni-tehnic-23-302. References Basan, R., Marohnić, T., 2024. A comprehensive evaluation of conventional methods for estimation of fatigue parameters of steels from their monotonic properties. International Journal of Fatigue 183, 108244. Bäumel, A., Seeger, T., 1990. Materials data for cyclic loading – Supplement 1. Amsterdam: Elsevier. Genel, K., 2004. Application of artificial neural network for predicting strain-life fatigue properties of steels on the basis of tensile tests. International Journal of Fatigue 26, 1027–1035. Marohnić, T., 2017. Estimation of Cyclic and Fatigue Parameters of Steels Based on Their Monotonic Properties Using Artificial Neural Networks. Ph.D. Thesis, University of Rijeka, Faculty of Engineering, Rijeka, Croatia. Muralidharan, U., Manson, S.S., 1988. A modified universal slopes equation for estimation of fatigue characteristics of metals. Journal of Engineering Materials and Technology 110, 55–58. Park, J.H., Song, J.H., 1995. Detailed evaluation of methods for estimation of fatigue properties. International Journal of Fatigue, 17, 5, 365–373. Roessle, M.L., Fatemi A., 2000. Strain-controlled fatigue properties of steels and some simple approximations. International Journal of Fatigue 22, 495–511. Soyer, M.A. , Kalaycı, C.B., Karakaş, Ö, 2022. Low-cycle fatigue parameters and fatigue life estimation of high-strength steels with artificial neural networks. Fatigue & Fracture of Engineering Materials & Structures 45, 3764–3785. Troshchenko, V.T., Khamaza, L.A., Apostolyuk, V.A., Babich, Y.N., 2011. Strain-life curves of steels and methods for determining the curve parameters. Part 2. Methods based on the use of artificial neural networks. Strength of Materials 43, 1–14. Wächter, M., Esderts, A., 2018. On the estimation of cyclic material properties – Part 2: Introduction of a new estimation method: Dedicated to Professor Dr.-Ing. Harald Zenner on the occasion of his eightieth birthday. Materials Testing 60, 953–959.