PSI - Issue 56

B. Kalita et al. / Procedia Structural Integrity 56 (2024) 105–110 B. Kalita/ Structural Integrity Procedia 00 (2019) 000–000

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4. Conclusion Fatigue crack growth rate behaviour of additively manufactured (fabricated by L-PBF) 17-4 PH SS alloy with respect to different specimen conditions was analyzed by using machine learning techniques. The following findings have been drawn from this study. It is suggested to calculate the growth of fatigue cracks using machine learning techniques, and the linear regression model has been used to investigate the link between the rate of fatigue crack growth and the stress intensity component. Utilizing testing data from various datasets, the trained model was verified. The findings reveal that the model can accurately predict the fatigue crack formation rate nonlinearities, and the performance of model-based fatigue crack growth is acceptable for diverse experimental results. It was observed that the Linear Regression algorithm has led to best R2 score and least mean squared error in predicting the FCGR of 17 4 PH SS alloy. In the feature importance analysis, apart from ∆K, the important parameters identified ar e Post Processing technique and Built Orientation for predicting the FCGR of 17-4 PH Stainless Steel alloy. Since the optimization of processing and post processing parameters are still in developing stage in metal additive manufacturing, data-driven models can help in establishing the appropriate set of input variables. An effective property predictive model can improve the understanding of FCGR mechanisms which would help to design the materials with high performance. Acknowledgements I would like to express my sincere gratitude towards my guide, Prof. R. Jayaganthan, Department of Engineering Design, Indian Institute of Technology Madras, for his invaluable advice and meticulous guidance throughout this project. I would also like to thank the members of the Materials Design & Additive Manufacturing lab, Department of Engineering Design, IIT Madras, for their timely advice and help during this study. References Rovinelli A, Sangid MD, Proudhon H, Ludwig W. Using machine learning and a data-driven approach to identify the small fatigue crack driving force in polycrystalline materials. npj Computational Materials. 2018 Jul 16;4(1):35. Melching D, Strohmann T, Requena G, Breitbarth E. Explainable machine learning for precise fatigue crack tip detection. Scientific Reports. 2022 Jun 9;12(1):9513. Konda N, Verma R, Jayaganthan R. Machine Learning based predictions of fatigue crack growth rate of additively manufactured Ti6Al4V. Metals. 2021 Dec 27;12(1):50. Raja A, Chukka ST, Jayaganthan R. Prediction of fatigue crack growth behaviour in ultrafine grained al 2014 alloy using machine learning. Metals. 2020 Oct 9;10(10):1349. Zhang L, Wang Z, Wang L, Zhang Z, Chen X, Meng L. Machine learning-based real-time visible fatigue crack growth detection. Digital Communications and Networks. 2021 Nov 1;7(4):551-8. Zhan Z, Li H. Machine learning based fatigue life prediction with effects of additive manufacturing process parameters for printed SS 316L. International Journal of Fatigue. 2021 Jan 1;142:105941. Bock, F.E.; Aydin, R.C.; Cyron, C.J.; Huber, N.; Kalidindi, S.R.; Klusemann, B. A Review of the Application of Machine Learning and Data Mining Approaches in Continuum Materials Mechanics. Front. Mater. 2019, 6, 1–23. Nezhadfar PD, Burford E, Anderson-Wedge K, Zhang B, Shao S, Daniewicz SR, Shamsaei N. Fatigue crack growth behavior of additively manufactured 17-4 PH stainless steel: Effects of build orientation and microstructure. International Journal of Fatigue. 2019 Jun 1;123:168-79 Daniewicz SR, Shamsaei N. An introduction to the fatigue and fracture behavior of additive manufactured parts. International Journal of Fatigue. 2017;2(94):167. Herzog D, Seyda V, Wycisk E, Emmelmann C. Additive manufacturing of metals. Acta Materialia. 2016 Sep 15;117:371-92. Yadollahi A, Shamsaei N. Additive manufacturing of fatigue resistant materials: Challenges and opportunities. International Journal of Fatigue. 2017 May 1;98:14-31. Yang KV, Rometsch P, Jarvis T, Rao J, Cao S, Davies C, Wu X. Porosity formation mechanisms and fatigue response in Al-Si-Mg alloys made by selective laser melting. Materials Science and Engineering: A. 2018 Jan 17;712:166-74. Romano S, Nezhadfar PD, Shamsaei N, Seifi M, Beretta S. High cycle fatigue behavior and life prediction for additively manufactured 17-4 PH stainless steel: Effect of sub-surface porosity and surface roughness. Theoretical and Applied Fracture Mechanics. 2020 Apr 1;106:102477 Kardomateas GA, Geubelle PH. Fatigue and fracture mechanics in aerospace structures. Encyclopedia of Aerospace Engineering. 2010 Dec 15. Stanzl-Tschegg SE. When do small fatigue cracks propagate and when are they arrested?. Corrosion Reviews. 2019 Oct 1;37(5):397-418. Schütz, W. Fatigue life prediction of aircraft structures—Past, present and future. Eng. Fract. Mech. 1974, 6, 745–762.