PSI - Issue 79

Qinghui Huang et al. / Procedia Structural Integrity 79 (2026) 291–297

297

0.0002, followed by the simple network model at 0.0013 and the double-hidden-layer model at 0.0006. MAE measures average absolute error, where lower values indicate more accurate individual predictions. Overall, among these three models, the single-hidden-layer neural network strikes an optimal balance between complexity and generalization capability, making it the preferred choice for predicting accumulated plastic strain under fatigue loading. 4. Conclusions This study established a CPFEM simulation model to capture the plastic strain accumulation process of LPBF GH4169 under cyclic loading. The proposed incremental neural network (INN) model effectively predicts accumulated plastic strain in LPBF GH4169 under fatigue loading. Among three constructed INN models (simple network, single-hidden-layer network, and double-hidden-layer network), the single-hidden-layer network demonstrated best predictive performance. It achieved an R² value of 0.9797 on the validation set, with mean absolute error (MAE) and root mean square error (RMSE) at 0.0002 and 0.0003, respectively. This model accurately captures nonlinear evolution of plastic strain with the number of cycles without requiring predefined empirical equations, thereby overcoming a key limitation of traditional fatigue prediction models. The multiscale framework integrating CPFEM and INN provides a novel solution for the accurate prediction of accumulated plastic strain and fatigue damage assessment in LPBF alloys. In this framework, CPFEM generates high-precision micromechanical data, while the neural networks learn nonlinear evolution patterns of plastic strain from this data, achieving deep integration of multiscale simulation and data-driven methodologies. Acknowledgements This work was funded by the International Partnership Program for Grand Challenges of Chinese Academy of Sciences (025GJHZ2023092GC) and the Strategic Priority Research Program of Chinese Academy of Sciences (XDB0620303). References Herzog D, Seyda V, Wycisk E, Emmelmann C, 2016. Additive manufacturing of metals. Acta Materialia 117, 371–392. Mellor S, Hao L, Zhang D, 2014. Additive manufacturing: A framework for implementation. International Journal of Production Economics 149, 194–201. Taghizadeh M, Zhu Z, 2024. A comprehensive review on metal laser additive manufacturing in space: Modeling and perspectives. Acta Astronautica 222, 403–421. Zhu L, Xue P, Lan Q, et al, 2021. Recent research and development status of laser cladding: A review. Optics & Laser Technology 138, 106915. Roters F, Eisenlohr P, Hantcherli L, Tjahjanto D, Bieler T.R, Raabe D, 2010. Overview of constitutive laws, kinematics, homogenization and multiscale methods in crystal plasticity finite-element modeling. Acta Materialia 58, 1152–1211. Hao X, Liu G, Wang Y, Wu S, Wang Z, 2022. Optimization of investment casting process for K477 superalloy aero-engine turbine nozzle by simulation and experiment. China Foundry 19, 351–358. Le B Q, Tran V H, Bui Q M, Pham V H, 2021. Fatigue life prediction of additively manufactured Ti-6Al-4V alloy using deep belief network back propagation model. Materials Science and Engineering: A 825, 141862. Hutchinson, J., 1976. Bounds and self-consistent-estimates for creep of polycrystalline materials. 348. Li L, Shen L, Proust G, 2015. Fatigue crack initiation life prediction for aluminium alloy 7075 using crystal plasticity finite element simulations. Mechanics of Materials 81, 84–93. Ma X, He X, Tu Z C, 2021. Prediction of fatigue-crack growth with neural network-based increment learning scheme. Engineering Fracture Mechanics 241, 107402.