PSI - Issue 79

Available online at www.sciencedirect.com

ScienceDirect

Procedia Structural Integrity 79 (2026) 291–297

© 2025 The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Abstract In this study, the micromechanical response of a representative volume element (RVE) under cyclic loading was simulated using the crystal plasticity finite element method (CPFEM) to obtain the local stress-strain response and accumulated plastic strain. Based on the high-fidelity data generated by CPFEM, an incremental neural network (INN) model was constructed. The INN model takes the load ratio and the current accumulated plastic strain as inputs to predict the corresponding accumulated plastic strain increment for a given number of cycles. Compared with traditional fatigue prediction models, this model does not require presetting empirical equations. The results demonstrate that this incremental learning approach can effectively capture the nonlinear evolution of plastic strain with the number of cycles. The developed single-hidden-layer INN model accurately predicts the plastic strain accumulation process in laser powder bed fusion (LPBF) GH4169 (Inconel 718) under cyclic loading and achieves the highest prediction accuracy. 28th International Conference on Fracture and Structural Integrity - 3rd Mediterranean Conference on Fracture and Structural Integrity Accumulated plastic strain prediction of LPBF alloy under fatigue load based on CPFEM and incremental neural network Qinghui Huang 1,2 , Pengbo Wang 1,2 , Lei Bian 3 , Meng Zhao 3 , Pablo Lopez-Crespo 4 , Ivan Sergeichev 5 , Filippo Berto 6 , Wenqi Liu 1,* , Guian Qian 1,* 1 State Key Laboratory of Nonlinear Mechanics (LNM), Institute of Mechanics, Chinese Academy of Sciences, Beijing, China 2 School of Engineering Science, University of Chinese Academy of Sciences, Beijing, China 3 Dynamic Machinery Institute of Inner Mongolia, Hohhot, China 4 Department of Civil and Materials Engineering, University of Malaga, Malaga, Spain 5 Skolkovo Institute of Science and Technology, Center for Materials Technologies, Moscow, Russia 6 Department. of Chemical Engineering Materials Environment, Sapienza University of Rome, Rome, Italy

Peer-review under responsibility of IGF28 - MedFract3 organizers Keywords: Neural network, Crystal plasticity, Accumulated plastic strain

* Corresponding author. Tel.: +86 135-5267-7285. E-mail address: liuwenqi@imech.ac.cn, qianguian@imech.ac.cn

2452-3216 © 2025 The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of IGF28 - MedFract3 organizers 10.1016/j.prostr.2025.12.336