PSI - Issue 60

S. Mahesh et al. / Procedia Structural Integrity 60 (2024) 382–389 Mahesh et al. / Structural Integrity Procedia 00 (2019) 000 – 000

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The present work is an initial study that provides a proof-of-concept for FCGR prediction using ML, and therefore further investigations are needed on using extensive data and other deep learning algorithms to predict FCGR irrespective of the material. Furthermore, similar prediction approach could be used to generate the entire FCGR curve. Acknowledgements The authors would like to thank the Principal and Head, Department of Mechanical Engineering, B.M.S. College of Engineering for constantly supporting the research activities at the College. One of the authors, Dr. Manjunatha acknowledges the support of the Director, CSIR-NAL, Bengaluru. References American Society for Testing and Materials, 2008. Standard test method for measurement of fatigue crack growth rates: designation: E 647-08. ASTM international. Anderson, T.L., 2017. Fracture mechanics: fundamentals and applications. CRC press. Broek, D., 2012. Elementary engineering fracture mechanics. Springer Science & Business Media. Brownlee, J., 2016. Machine learning mastery with Python: understand your data, create accurate models, and work projects end-to-end. Machine Learning Mastery. Boyce, B.L. and Ritchie, R.O., 2001. Effect of load ratio and maximum stress intensity on the fatigue threshold in Ti – 6Al – 4V. Engineering Fracture Mechanics, 68(2), pp.129-147. Boyer, H.E. ed., 1985. Atlas of fatigue curves. Asm International. Forman, R.G., Shivakumar, V., Cardinal, J.W., Williams, L.C. and McKeighan, P.C., 2005. Fatigue crack growth database for damage tolerance analysis (No. PB2005-110675). Géron, A., 2022. Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow. " O'Reilly Media, Inc.". Goodfellow, I., Bengio, Y. and Courville, A., 2016. Deep learning. MIT press. Kujawski, D., 2001. A fatigue crack driving force parameter with load ratio effects. International Journal of Fatigue, 23, pp.239-246. Malipatil, S.G., Majila, A.N., Fernando, D.C., Manjuprasad, M. and Manjunatha, C.M., 2021. Fatigue crack growth behaviour of a nickel base super alloy GTM720 under cold-TURBISTAN spectrum load sequence. Theoretical and Applied Fracture Mechanics, 112, p.102913. Malipatil, S.G., Majila, A.N., Fernando, D.C., Manjuprasad, M. and Manjunatha, C.M., 2021. Damage tolerance behavior of a nickel-based super-alloy GTM718 under cold-TURBISTAN variable amplitude loads. Transactions of the Indian Institute of Metals, 74, pp.901-907. Mohanty, J.R., Verma, B.B., Parhi, D.R.K. and Ray, P.K., 2009. Application of artificial neural network for predicting fatigue crack propagation life of aluminum alloys. Raja, A., Chukka, S.T. and Jayaganthan, R., 2020. Prediction of fatigue crack growth behaviour in ultrafine grained al 2014 alloy using machine learning. Metals, 10(10), p.1349. Ritchie, R.O., 1979. Near-threshold fatigue-crack propagation in steels. International Metals Reviews, 24(1), pp.205-230. Skansi, S., 2018. Introduction to Deep Learning: from logical calculus to artificial intelligence. Springer. Wang, H., Zhang, W., Sun, F. and Zhang, W., 2017. A comparison study of machine learning based algorithms for fatigue crack growth calculation. Materials, 10(5), p.543. Zhang, L., Wang, Z., Wang, L., Zhang, Z., Chen, X. and Meng, L., 2021. Machine learning-based real-time visible fatigue crack growth detection. Digital Communications and Networks, 7(4), pp.551-558.