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<p>Topology optimization techniques provide a way to find the optimal topologies for structures automatically. The Moving Morphable Components (MMC) approach, depicting topology explicitly, has its unique advantages in geometric description. However, the geometry-driven feature of the MMC method unfortunately introduces serious challenges when dealing with Stress-Constrained Problems (SCPs). Existing strategies cannot well control the inherent instability in the optimization processes since the responsive stress is highly sensitive to the topologies. The Stabilized Time-Series Moving Morphable Components (STSMMC) approach, which makes use of trust region based moving asymptotes, is adopted in this article, showcasing better stability in solving 3D SCPs. Numerical examples with large values of the p-norm parameter and a large number of elements indicate the outstanding performance of the STSMMC approach in solving the SCPs.</p>

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3D Stress-constrained topology optimization via stabilized time-series moving morphable components approach

  • 期刊论文

  • 发表日期：

    2025年10月4日

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  • Zonghao Li,
  • Xueyan Hu,
  • Weiqiu Chen

摘要

Topology optimization techniques provide a way to find the optimal topologies for structures automatically. The Moving Morphable Components (MMC) approach, depicting topology explicitly, has its unique advantages in geometric description. However, the geometry-driven feature of the MMC method unfortunately introduces serious challenges when dealing with Stress-Constrained Problems (SCPs). Existing strategies cannot well control the inherent instability in the optimization processes since the responsive stress is highly sensitive to the topologies. The Stabilized Time-Series Moving Morphable Components (STSMMC) approach, which makes use of trust region based moving asymptotes, is adopted in this article, showcasing better stability in solving 3D SCPs. Numerical examples with large values of the p-norm parameter and a large number of elements indicate the outstanding performance of the STSMMC approach in solving the SCPs.