PSI - Issue 42

Zafer Yüce et al. / Procedia Structural Integrity 42 (2022) 663–671 Yuce Z., Yayla P., Taskin A./ Structural Integrity Procedia 00 (2019) 000 – 000

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model is taken into consideration. It has been shown that the fatigue index of individual aircraft can be calculated, and predictive maintenance programs may be launched with a low error value. Additional studies, such as investigating thru crack configuration and including the effects of secondary bending, will be required to develop a complete picture of joint CG life estimation. References Aicher W, Branger J, van Dijk GM, Ertelt J, Hück M, de Jonge JB, E. A. (1976). Description of a fighter aircraft loading standard for fatigue evaluation FALSTAFF, Common Report of FCW Emmen, LBF, NRL, IABG (1976) Al-Assadi, M., El Kadi, H. A., & Deiab, I. M. (2011). Using Artificial Neural Networks to Predict the Fatigue Life of Different Composite Materials Including the Stress Ratio Effect. Applied Composite Materials, 18(4), 297 – 309. https://doi.org/10.1007/s10443-010-9158-7 Barbosa, J. F., Correia, J. A. F. O., Júnior, R. C. S. F., & Jesus, A. M. P. De. (2020). Fatigue life prediction of metallic materials considering mean stress effects by means of an artificial neural network. International Journal of Fatigue, 135, 105527. https://doi.org/https://doi.org/10.1016/j.ijfatigue.2020.105527 ESDU 98012, Flexibility of, and load distribution in, multi-bolt lap joints subject to in-plane axial loads. (2002), Engineering Sciences Data Unit (Endorsed by The Royal Aeronautical Society), London. 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(10), 1027 – 1035. https://doi.org/https://doi.org/10.1016/j.ijfatigue.2004.03.009 Huth, H. (1984). Zum Einfluß der Nietnachgiebigkeit mehrreihiger Nietverbindungen auf die Lastübertragungs- und Lebensdauervorhersage. JSSG-2006, JOINT SERVICE SPECIFICATION GUIDE: AIRCRAFT STRUCTURES. (1998). Marquardt, C., & Zenner, H. (2005). Lifetime calculation under variable amplitude loading with the application of artificial neural networks. International Journal of Fatigue, 27(8), 920 – 927. https://doi.org/https://doi.org/10.1016/j.ijfatigue.2004.12.010 Mathew, M. D., Kim, D. W., & Ryu, W.-S. (2008). A neural network model to predict low cycle fatigue life of nitrogen-alloyed 316L stainless steel. Materials Science and Engineering: A, 474(1), 247 – 253. https://doi.org/https://doi.org/10.1016/j.msea.2007.04.018 Matsuichi, M., & Endo, T. (1968). Fatigue of metals subjected to varying stress, Japan Society of Mechanical Engineers, Fukuoka, Japan, 68(2), 37-40. Pleune, T. T., & Chopra, O. K. (2000). Using artificial neural networks to predict the fatigue life of carbon and low-alloy steels. Nuclear Engineering and Design, 197(1), 1 – 12. https://doi.org/https://doi.org/10.1016/S0029-5493(99)00252-6 Srinivasan, V. S., Valsan, M., Bhanu Sankara Rao, K., Mannan, S. L., & Raj, B. (2003). Low cycle fatigue and creep – fatigue interaction behavior of 316L(N) stainless steel and life prediction by artificial neural network approach. International Journal of Fatigue, 25(12), 1327 – 1338. https://doi.org/https://doi.org/10.1016/S0142-1123(03)00064-1 Vassilopoulos, A. P., Georgopoulos, E. F., & Dionysopoulos, V. (2007). Artificial neural networks in spectrum fatigue life prediction of composite materials. International Journal of Fatigue, 29(1), 20 – 29. https://doi.org/https://doi.org/10.1016/j.ijfatigue.2006.03.004 Yang, J., Kang, G., Liu, Y., Chen, K., & Kan, Q. (2020). Life prediction for rate-dependent low-cycle fatigue of PA6 polymer considering ratchetting: Semi-empirical model and neural network based approach. International Journal of Fatigue, 136, 105619. https://doi.org/https://doi.org/10.1016/j.ijfatigue.2020.105619