Characterization and Hybrid Modeling of Fatigue Evolution of Additively Manufactured Lattice Structures

Dear Colleagues,

Lattice structures produced via powder-bed fusion variants (such as Laser Powder Bed Fusion and Electron Beam Melting) offer extreme design flexibility and severe lightweighting potential across the aerospace, automotive, and biomedical sectors. However, their long-term durability under fatigue loading is deeply compromised by complex microstructural heterogeneities, surface roughness, residual stresses, and sub-surface or lack-of-fusion porosities inherited from the layer-by-layer consolidation process. To unravel these complex damage mechanisms, this Special Issue showcases cutting-edge hybrid measurement and testing platforms. Key focus areas include micro-computed tomography (µ-CT) for 3D pore morphological tracking, digital image correlation (DIC) for deformation and damage analysis, and high-frequency testing to capture localized cyclic plasticity, strain localization, and crack propagation thresholds from high-cycle fatigue (HCF) to very-high-cycle fatigue (VHCF) regimes. Complementing physical trials, data-driven methodologies bridge the accuracy–interpretability gap. Research spotlights the deployment of physics-informed neural networks (PINNs), and machine learning regression ensembles (e.g., Random Forest, XGBoost) to predict fatigue limits and map non-linear process–structure–property–damage relationships. Advanced optimization algorithms, and Genetic Algorithms, are leveraged alongside Explainable AI tools (such as SHAP value analysis) to ensure deterministic parameter selection. Furthermore, this Special Issue highlights how integrating surrogate modeling and multi-scale finite element methods (CPFEM) into digital twin architectures accelerates the robust, uncertainty-aware design of certifiable, damage-tolerant AM structures.

Dr. Mustafa Awd
Prof. Dr. Frank Walther
Guest Editors

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