PSI - Issue 48

Bernadett Spisák et al. / Procedia Structural Integrity 48 (2023) 326–333 Spisák et al / Structural Integrity Procedia 00 (2023) 000 – 000

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size was also checked, and it and it has been established that the way in which the pre-cracking is given (averaged vs real) has a large influence on the final force displacement curve. Beside this the size of the geometry has also some effect on it, and it should be investigated in more detail. A major advantage of the VCCT model is that the crack does not propagate as elements are removed from the simulation but opens the mesh when the set critical damage value is reached, allowing continuous evaluation of the J integral by continuously varying the crack tip. As confirmed in Summary Table 2, this method can calculate the J integral with very good accuracy, which is well suited for non-standard cases too, for example in case of the shown miniaturized CT specimen where the difference between the results were only 6%. In future work, it is planned to apply this method to the simulation of the brittle-ductile transition zone. The method may provide a good way to determine the transition temperature of a given material, but further studies and simulation developments are needed. Acknowledgements This project has received funding from the Euratom research and training programme 2020-2024 under grant agreement No 900014 (H2020 FRACTESUS). The views and opinions expressed herein do not necessarily reflect those of the European Commission. References Abbassi, F., Belhadj, T., Mistou, S, Zghal, A., 2013. Parameter identification of a mechanical ductile damage using Artificial Neural Networks in sheet metal forming, Material & Design, 45, 605-615 ASTM E1820-20: Standard Test Method for Measurement of Fracture Toughness Bhadeshia, H., K., D., H., 1999. Neural Networks in Materials Science, ISIJ International, 39 (10), 966-979. Guan, C.P., Jin, H.P., 2012. Determination of Residual Stress and Strain-Hardening Exponent Using Artificial Neural Networks, Advanced Materials Research 472 – 475, 332 – 335. Guo, Z., Malinov, S., Sha, W., 2005. Modelling beta transus temperature of titanium alloys using artificial neural network Computational Materials Science 32, 1 – 12. Gurson, A., 1977. Continuum theory of ductile rupture by void nucleation and growth: part 1. Yield criteria and flow rules for porous ductile media, Journal of Engineering Materials and Technology, 99, 2 – 15. Lucon, E., Scibetta, M., Chaouadi, R., Walle, E., 2006. Use of Miniaturized Compact Tension Specimens for Fracture Toughness Measurements in the Upper Shelf Regime, Journal of ASTM International 3 (1) Krueger, R. 2004. Virtual crack closure technique: History, approach, and applications, ASME. Appl. Mech. Rev. 57(2), 109 – 143 Sánchez, M., Cicero, S., Kirk, M., Altstadt, E., Server, W., Yamamoto, M., 2023. Using Mini-CT Specimens for the Fracture Characterization of Ferritic Steels within the Ductile to Brittle Transition Range: A Review, Metals 13 (1) Sirkiä, L. 2017. Applicability of miniature Compact Tension specimens for fracture toughness determination in ductile-brittle transition range. Thesis Sokolov, M., A., 2018. The Fracture Toughness Evaluation of Mini-CT specimen Test Results of the Irradiated Midland RPV Beltline Material, Light Water Reactor Sustainability Program, ORNL/TM-2018/509 Sokolov, M., A., 2022. Use of Mini-CT Specimens for Fracture Toughness Characterization of Irradiated Highly Embrittled Weld, ASME 2022 Pressure Vessels & Piping Conference, Las Vegas, Nevada, USA. July 17 – 22, 2022. Shikalgar, T., D., Dutta, B., K., Chattopadhyay, J., 2020. Analysis of p-SPT specimens using Gurson parameters ascertained by Artificial Neural Network, Engineering Fracture Mechanics, 240 Tvergaard, V., Needleman, A., 1984.Analysis of the cup-cone fracture in a round tensile bar, Acta Metallurgica, 32 (1), 157-169