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<p>Lattice structures have been widely used due to their potential in lightweight structural applications. In this study, a new approach for the design and optimization of a lattice structure fabricated by additive manufacturing (AM) was proposed to decrease volume under acceptable stress levels. A solid gear body was converted into a lightweight lattice structure and optimized employing a parametric design approach and topology optimization. Initially, Artificial Neural Network (ANN) and Kriging surrogate models were employed to explain the relationship between unit cell parameters, namely strut diameter and cell dimension, and stress levels and volume. Subsequently, the ANN model exhibiting the lower prediction error was employed in conjunction with a genetic algorithm (GA) to determine the optimal unit cell parameters. Using these optimal cell parameters as initial design parameters for the gear, topology optimization was then applied to further reduce stress concentrations. The final gear was fabricated from a mixture of powders of AlSi10Mg and NiCrMo using a laser powder bed fusion AM process. The AlSi10Mg matrix composite was reinforced in-situ with 2.5 wt% NiCrMo particles. Following this approach for lattice structure optimization, the total mass of a gear was reduced by 46.3%, while providing proper stiffness.</p>

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Additive manufacturing of lightweight lattice structure gears designed by topology optimization and surrogate modeling

  • 期刊论文

  • 发表日期：

    2026年7月30日

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  • Riad Ramadani,
  • Elif Elçin Günay,
  • Snehashis Pal,
  • Jožef Predan,
  • Marko Kegl,
  • Igor Drstvenšek,
  • Gül E. Okudan Kremer

摘要

Lattice structures have been widely used due to their potential in lightweight structural applications. In this study, a new approach for the design and optimization of a lattice structure fabricated by additive manufacturing (AM) was proposed to decrease volume under acceptable stress levels. A solid gear body was converted into a lightweight lattice structure and optimized employing a parametric design approach and topology optimization. Initially, Artificial Neural Network (ANN) and Kriging surrogate models were employed to explain the relationship between unit cell parameters, namely strut diameter and cell dimension, and stress levels and volume. Subsequently, the ANN model exhibiting the lower prediction error was employed in conjunction with a genetic algorithm (GA) to determine the optimal unit cell parameters. Using these optimal cell parameters as initial design parameters for the gear, topology optimization was then applied to further reduce stress concentrations. The final gear was fabricated from a mixture of powders of AlSi10Mg and NiCrMo using a laser powder bed fusion AM process. The AlSi10Mg matrix composite was reinforced in-situ with 2.5 wt% NiCrMo particles. Following this approach for lattice structure optimization, the total mass of a gear was reduced by 46.3%, while providing proper stiffness.