PSI - Issue 42

Robert Basan et al. / Procedia Structural Integrity 42 (2022) 655–662 R. Basan et al. / Structural Integrity Procedia 00 (2019) 000–000

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reference for evaluations of newly developed estimation methods as well as of predictive models based on machine learning. In extension of presented work, inclusion of additional estimation methods, both conventional ones and machine learning-based ones will be included in analyses. Acknowledgements This work has been supported in part by Croatian Science Foundation under the project IP-2020-02-5764 and by the University of Rijeka under the project number uniri-tehnic-18-116. References Basan, R., Franulović, M., Prebil, I., Črnjarić-Žic, N., 2011. Analysis of strain-life fatigue parameters and behaviour of different groups of metallic materials. International Journal of Fatigue, 33, 484–491. Basan, R., Rubeša, D., Franulović, M., Marohnić, T., 2015. Some considerations on the evaluation of methods for the estimation of fatigue parameters from monotonic properties. Procedia Engineering, 101, 18-25. Bäumel, A., Seeger, T., 1990. Materials data for cyclic loading – Supplement 1. Amsterdam: Elsevier. Derrick, C., Fatemi, A., 2022. Correlations of fatigue strength of additively manufactured metals with hardness and defect size. International Journal of Fatigue, 162, 106920. Hätscher, A., Seeger, T., Zenner, H., 2007. Abschätzung von zyklischen Werkstoffkennwerten – Erweiterung und Vergleich bisheriger Ansätze. MP Material Testing, 49(3), 2–14. Jeon, W. S., Song, J.H., 2002. An expert system for estimation of fatigue properties of metallic materials. International Journal of Fatigue, 24, 685–698. Lee, K. S., Song, J. H., 2006. Estimation methods for strain life fatigue properties from hardness. International Journal of Fatigue, 28, 386–400. Linka, K., Hillgärtner, M., Abdolazizi, K. P., Aydin, R. C., Itskov, M., & Cyron, C. J., 2021. Constitutive artificial neural networks: A fast and general approach to predictive data-driven constitutive modeling by deep learning. Journal of Computational Physics, 429, 110010. Manson, S. S., 1965. Fatigue: A complex subject – Some simple approximations. Exp Mech SESA. 5, 7, 193–226. Marohnić, T., Basan, R., 2018. Estimation of cyclic behavior of unalloyed, low-alloy and high-alloy steels based on relevant monotonic properties using artificial neural networks. Materialwissenschaft Und Werkstofftechnik, 49, 3. Meggiolaro, M. A., Castro, J. T. P., 2004. Statistical evaluation of strain-life fatigue crack initiation predictions. International Journal of Fatigue, 26, 463–476. 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. Ong, J. H., 1993. An evaluation of existing methods for the prediction of axial fatigue life from tensile data. International Journal of Fatigue, 15, 1, 13–19. Ong. J. H., 1993. An improved technique for the prediction of axial fatigue life from tensile data. International Journal of Fatigue, 15, 213–219. 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. Socie, D. F., Mitchell, M. R., Caulfield, E. M., 1977. Fundamentals of modern fatigue analysis – FCP Report. Urbana: University of Illinois. Tomasella, A., Dsoki, C., Hanselka, H., & Kaufmann, H., 2011, A Computational Estimation of Cyclic Material Properties Using Artificial Neural Networks. Procedia Engineering, 10, 439–445. Troshchenko, V., Khamaza, L., Apostolyuk, V., & Babich, Y., 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 1: Quality of known estimation methods. Materials Testing, 60(10), 945-952. Wächter, M., Esderts, A., 2018. On the estimation of cyclic material properties – Part 2: Introduction of a new estimation method. Materials Testing, 60(10), 953-959. Yadegari, P., Fällgren,C., Beier,H. T., Vormwald, M., Kleeman, A., 2022. Extension of methods for estimating the fatigue strength of components made of ultra-high strength steels, International Journal of Fatigue, 107325.