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<p>This study establishes a computationally efficient and highly accurate framework for predicting and optimizing the geometrical fidelity of metallic lattice structures in additive manufacturing (AM). While lattice structures offer unique functional capabilities through their microscopic configurations, their practical application is often hindered by significant geometrical deviations from the intended design. The primary contribution of this work is the development of a novel integrated approach that bridges the gap between conventional thermomechanical simulations and actual as-fabricated geometries by explicitly accounting for melt pool dynamics and surface tension (bulge) effects. Although thermomechanical simulations are commonly used, they alone often fail to capture the critical influences of melt pool size and surface tension on the dimensional accuracy of fine lattice beams. By combining thermomechanical analysis with detailed melt pool and bulge investigations, the proposed framework achieves remarkably improved prediction accuracy while remaining computationally practical for engineering applications. Experimental validation of fabricated AlSi10Mg lattice specimens confirmed the excellent accuracy of this method 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.</p>

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Accurate geometrical prediction and process optimization of additively manufactured metallic lattice structures via integrated thermomechanical and melt pool analysis

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月18日

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  • Natsuki Tsushima,
  • Masako Kita,
  • Isamu Matsubara,
  • Masao Ohishi,
  • Keiko Kaneko,
  • Kenji Mitsui,
  • Tomio Kanzawa,
  • Ryo Higuchi,
  • Koji Yamamoto

摘要

This study establishes a computationally efficient and highly accurate framework for predicting and optimizing the geometrical fidelity of metallic lattice structures in additive manufacturing (AM). While lattice structures offer unique functional capabilities through their microscopic configurations, their practical application is often hindered by significant geometrical deviations from the intended design. The primary contribution of this work is the development of a novel integrated approach that bridges the gap between conventional thermomechanical simulations and actual as-fabricated geometries by explicitly accounting for melt pool dynamics and surface tension (bulge) effects. Although thermomechanical simulations are commonly used, they alone often fail to capture the critical influences of melt pool size and surface tension on the dimensional accuracy of fine lattice beams. By combining thermomechanical analysis with detailed melt pool and bulge investigations, the proposed framework achieves remarkably improved prediction accuracy while remaining computationally practical for engineering applications. Experimental validation of fabricated AlSi10Mg lattice specimens confirmed the excellent accuracy of this method 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.