PSI - Issue 79

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

292

1. Introduction Metal alloys fabricated by laser powder bed fusion (LPBF) technology exhibit complex mechanical responses due to their unique microstructures, such as pores, lack of fusion defects, and anisotropic grain orientation (Herzog et al. (2016)). Particularly under cyclic loading, the accumulation of microscopic plastic strain directly influences the fatigue life and durability of the material (Mellor et al. (2014), Taghizadeh and Zhu (2024)). Therefore, accurately predicting the accumulated plastic strain of LPBF alloys under fatigue loads is crucial for evaluating the reliability of additively manufactured components. The microstructure of LPBF GH4169 (Inconel 718) exhibits significant spatial non-uniformity. During the LPBF process, the coarse-grained and fine-grained regions that tend to form present a spatially ordered bimodal structure (Zhu et al. (2021)). This structure leads to a highly localized plastic strain distribution of the material during cyclic deformation. In recent years, the crystal plastic finite element method (CPFEM) has become a powerful tool for studying the micromechanical behavior of metallic materials (Roters et al. (2010)). By coupling the crystal plasticity constitutive and finite element simulation, CPFEM can reproduce the local stress-strain response and accumulated plastic strain of materials under cyclic loading. For instance, researchers have analyzed the influence of different defect sizes and locations on fatigue indicator parameters (FIPs) through CPFEM simulation, thereby establishing correlations with fatigue life (Hao et al. (2022)). However, the computational cost of CPFEM is high, making it difficult to be directly applied to fatigue analysis and life prediction at the engineering component level. Meanwhile, data-driven methods, especially artificial neural networks (ANNs), have shown great potential in predicting the fatigue behavior of materials. For example, a deep belief network-back propagation (DBN-BP) model has been successfully used to predict the fatigue life of the Ti-6Al-4V fabricated by LPBF in the high-cycle, low-cycle and very-high-cycle fatigue regimes (Le et al. (2021)). However, most existing data-driven models mainly focus on the prediction of macroscopic fatigue life ( N f ), but fail to capture the incremental evolution of plastic strain during cyclic deformation. To address these challenges, this paper innovatively proposes a multiscale framework that combines CPFEM and neural networks (NNs) to accurately predict the accumulated plastic strain of LPBF GH4169 under fatigue loading. This framework fully exploits the advantages of CPFEM in revealing microscopic mechanical responses and the ability of incremental neural networks (INNs) to capture nonlinear temporal evolution, providing a new solution for the accurate fatigue damage assessment and life prediction of in LPBF alloys.

Nomenclature RVE

representative volume element CPFEM crystal plasticity finite element method INN incremental neural network LPBF laser powder bed fusion DBN-BP deep belief network–back propagation RMSE root mean square error MAE mean absolute error

2. Method This paper introduces a research framework that combines multiscale simulation with data-driven methodologies. The core lies in generating high-fidelity data through CPFEM simulation and then training an incremental neural network (INN) to predict the accumulated plastic strain under fatigue loads of LPBF alloys. 2.1 CPFEM theory and simulation Crystal plasticity finite element (CPFE) theory is an advanced numerical simulation method based on material microstructure. It integrates the physical mechanisms of crystal plasticity with the macroscopic finite element