Accurate Geometrical Prediction and Process Optimization of Additively Manufactured Metallic Lattice Structures via Integrated Thermomechanical and Melt Pool Analysis

Lattice structures, drawing attention for their ability to generate unique functionalities and control macroscopic structural properties through artificially designed microscopic configurations, are effectively realized by additive manufacturing (AM). However, the geometrical accuracy of AM lattice structures often deviates from design, impacting their intended structural performance. While thermomechanical simulations are commonly used for deviation prediction, they alone may not fully capture these discrepancies. This study uniquely demonstrates that for lattice structures, melt pool size and surface tension (bulge) significantly influence dimensional accuracy. To address this, we introduce a novel integrated approach combining thermomechanical simulations with detailed melt pool and bulge analyses to precisely predict geometrical variations in AM metallic lattice structures. This approach offers a significant advantage over higher-fidelity methods by achieving remarkably improved prediction accuracy with a simple and computationally practical framework, making it highly suitable for engineering applications. Experimental validation of fabricated AlSi10Mg lattice specimens confirmed this integrated method's excellent accuracy in predicting geometries. Based on this precise prediction, we successfully optimized AM process parameters (laser power, scan speed, and beam offset). Optimized lattice specimens exhibited significantly improved geometrical accuracy, with most parameters differing by less than 5% from the CAD model, unlike unoptimized samples. Furthermore, the study revealed that geometrical deviations can compromise structural integrity by affecting effective stiffness, highlighting that the proposed prediction and optimization approach ensures both precise geometry and reliable structural performance for lattice structures.