PSI - Issue 75

Jan Schubnell et al. / Procedia Structural Integrity 75 (2025) 94–101 Schubnell/ Structural Integrity Procedia (2025)

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References

Agrawal, A. et al. (2014) ‘Exploration of data science techniques to predict fatigue strength of steel from composition and processing parameters’, Integrating Materials and Manufacturing Innovation , 3(1), pp. 90 – 108. doi: 10.1186/2193-9772-3-8/FIGURES/7. Agrawal, A. and Choudhary, A. (2018) ‘An online tool for predicting fatigue strength of steel alloys based on ensemble data mining’, International Journal of Fatigue , 113, pp. 389 – 400. doi: 10.1016/J.IJFATIGUE.2018.04.017. Awad, M. and Khanna, R. (2015) ‘Efficient learning machines: Theories, concepts, and applications for engineers and system designers’, Efficient Learning Machines: Theories, Concepts, and Applications for Engineers and System Designers , pp. 1 – 248. doi: 10.1007/978-1-4302 5990-9/COVER. Bartsch, H. et al. (2020) ‘Analysis of fatigue test data to reassess EN 1993 -1- 9 detail categories’, Steel Construction , 13(4), pp. 280 – 293. doi: 10.1002/STCO.202000019. Baumgartner, J., Hobbacher, A. F. and Rennert, R. (2020) ‘Fatigue assessment of welded thin sheets with the notch stress appr oach – Proposal for recommendations’, International Journal of Fatigue , 140, p. 105844. doi: 10.1016/J.IJFATIGUE.2020.105844. Berger, C. et al. (2006) FKM-Richtlinie: Bruchmechanischer Festigkeitsnachweis für Maschinenbauteile . 6. Auflage. Frankfurt: VDMA Verlag. Breiman, L. (2001) ‘Random forests’, Machine Learning , 45(1), pp. 5 – 32. doi: 10.1023/A:1010933404324/METRICS. Chen, J. and Liu, Y. (2022) ‘Fatigue modeling using neural networks: A comprehensive review’, Fatigue & Fracture of Engineering Materials & Structures , 45(4), pp. 945 – 979. doi: 10.1111/FFE.13640. DIN 50100-2022-12: Schwingfestigkeitsversuch – Durchführung und Auswertung von zyklischen Versuchen mit konstanter Lastamplitude für metallische Werkstoffproben und Bauteile (2022). Berlin. Eurocode 3: Design of steel structures -Part 1-9: Fatigue, 1993-1-9:2005 (2009). Brussels: European Commitee of Standardization. Feron, B. (1979) ‘Bootstrap Methods Another Look at the Jackknife’, The Annals of Statistics , 7(1), pp. 1 – 26. doi: https://doi.org/10.1214/aos/1176344552. Fiedler, M. et al. (2019) FKM-Richtlinie: Rechnerischer Festigkeitsnachweis unter expliziter Erfassung nichtlinearen Werkstoffverformungsverhaltens Für Bauteile aus Stahl, Stahlguss und Aluminiumknetlegierungen . 1th edn. VDMA Verlag. Fliegener, J. Rosenberger, M. Luke, J. Domínguez, J. Morgado, H.U. Kobialka, T. Kraft, J. T. (2024) ‘Digital Methods for the Fatigue Assessment of Engineering Steels’, Advanced Engineering Materials , submitted(special issue ‘Digitalization in Materials’). Fliegener, S. et al. (2023) ‘Digitale Methoden für die Lebensdauerbewertung am Beispiel hochfester Stähle’, in Deutscher Verband für Materialforschung und -prüfung, Arbeitskreis Betriebsfestigkeit (DVM Tagung) . doi: 10.48447/BF-2023-072. Furuya, Y. et al. (2019) ‘Catalogue of NIMS fatigue data sheets’, Science and Technology of Advanced Materials , 20(1), pp. 1055 – 1072. doi: 10.1080/14686996.2019.1680574. Haibach, E. (2006) Betriebsfestigkeit: Verfahren und Daten zur Bauteilberechnung . 3rd edn. Wiesbaden: Springer. Lee, J. A., Almond, D. P. and Harris, B. (1999) ‘The use of neural networks for the prediction of fatigue lives of composite materials’, Composites Part A: Applied Science and Manufacturing , 30(10), pp. 1159 – 1169. doi: 10.1016/S1359-835X(99)00027-5. Liu, C. et al. (2023) ‘Prediction of the Fatigue Strength of Steel Based on Interpretable Machine Learning’, Materials , 16(23), p. 7354. doi: 10.3390/MA16237354/S1. Pedregosa, F. (2011) ‘Scikit - learn: Machine Learning in Python’, JMLR , 12, pp. 2725 – 2839. Radaj, D. and Vormwald, M. (2007) Ermüdungsfestigkeit, Grundlagen für Ingenieure . 3te, neube edn. Springer Berlin Heidelberg New York. Rennert, R. et al. (2020) FKM-guideline Analytical strength assessment of components in mechanical engineering made of Steel, Cast Iron and Aluminium Materials . 7 th, revi. Frankfurt / Main, Germany: Forschungskuratorium Maschinenbau (FKM). Ross, C. T. (1993) ‘Best practice guidelines for developing neural computing applications—an overview’, UK: Ministry of Defense Procurement Executive . Schubnell, J. et al. (2025) ‘Data - driven fatigue assessment of welded steel joints based on transfer learning’, Welding in the World , pp. 1 – 16. doi: 10.1007/S40194-025-01967-X/FIGURES/8. Uygur, I. et al. (2014) ‘Fatigue life predictions of metal matrix composites using artificial neural networks’, Archives of Metallurgy and Materials , 59(1), pp. 97 – 103. doi: 10.2478/AMM-2014-0016. Wang, H. et al. (2023) ‘Machine learning -based fatigue life prediction of metal materials: Perspectives of physics-informed and data-driven hybrid methods’, Engineering Fracture Mechanics , 284, p. 109242. doi: 10.1016/J.ENGFRACMECH.2023.109242. Weichert, D. et al. (2022) ‘Robustness in Fatigue Strength Estimation’. Available at: https://arxiv.org/abs/2212.01136v1 (Accessed: 15 April 2025). Zhao, W. (2021) ‘Calibration of design fatigue factors for offshore structures based on fatigue test database’, International Journal of Fatigue , 145, p. 106075. doi: 10.1016/J.IJFATIGUE.2020.106075.

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