Uncertainty quantification of Additive Manufactured Lattice Structures under Process-induced Geometric Defects

Abstract

Lattice structures, characterized by their interconnected grid-like arrangements of struts or beams, are widely used in diverse engineering applications, including aerospace components and biomechanical scaffolds. Additive Manufacturing (AM) enables the fabrication of these complex geometries, but inherent limitations of the layer-by-layer stacking process—such as variations in strut diameters, material parameters, and geometric imperfections—introduce significant uncertainties that compromise structural reliability. This study establishes a framework for quantifying the uncertainties of process-induced geometric defects in AM lattice structures, focusing on the interplay between geometric defects (e.g., thickness deviations, node misalignments) and mechanical properties. A hybrid methodology integrating stochastic finite element modeling, high-resolution imaging, and machine learning-based surrogate models is employed to map defect distributions to mechanical performance. By correlating designable parameters of lattice structures with experimental data, failure responses of lattices under quasi-static loading are probabilistically predicted, revealing how geometric and material variability amplify stress concentrations and collapse mode deviations. The results underscore the critical need to address multi-source uncertainties in design workflows to ensure reliability under operational demands. This work advances optimization design techniques for AM lattices, enhancing their performance predictability in safety-critical applications such as lightweight aeronautical structural system.

Bio

Dr. Jingwen Song serves as an associate professor in Northwestern Polytechnical University, School of Mechanical Engineering since 2022. She obtained her PhD from Leibniz University Hannover in Germany in 2020, and the doctoral dissertation was awarded “Summa Cum Laude”. After graduation, she worked as a research assistant professor in Tokyo City University in Japan until the end of 2021. Her research is focused on data science for risk and reliability analysis, model calibration, sensitivity analysis, as well as the modelling and optimization design for complex structural systems. Within the broad field, she has a particular penchant for exploring efficient computational methods in forward and backward uncertainty quantification by means of advanced Monte Carlo simulation methods and Bayesian machine learning approaches. Dr. Song is also a member of the Early Career Editorial Board of Computers & Structures. She has published more than 20 peer-reviewed publications, and organized several sessions in international conferences.