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<p>Architected materials with advanced functionalities are increasingly employed in fields such as biomedicine and robotics. While periodic designs have been predominant, disordered materials inspired by natural irregularities have recently gained prominence for their potential to high damage tolerance, isotropy, and imperfection insensitivity. However, the advantages of such irregular materials remain debated. Here, inspired by a pioneering stochastic growth rule, we present an irregular growth strategy that uses a limited set of cells to engineer mechanical stealth, achieving both static cloaking and camouflage within a narrow error tolerance—a feat challenging for periodic designs. Our approach demonstrates adaptability to diverse boundary loads and void configurations, ensuring reliable performance without requiring extensive datasets. Arbitrary cloaks generated with minimal samples retain camouflage under varied conditions, including mutual camouflage between targets with disparate void shapes. This framework is further extended to three-dimensional scenarios, highlighting its potential for cloaking and camouflage across multiscale applications.</p>

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Static mechanical cloaking and camouflage from disorder

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年10月6日

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  • Zhou Yang,
  • Jianlin Yi,
  • Fenglei Li,
  • Zheng Li,
  • Lin Ye,
  • Bing Li,
  • Johan Christensen

摘要

Architected materials with advanced functionalities are increasingly employed in fields such as biomedicine and robotics. While periodic designs have been predominant, disordered materials inspired by natural irregularities have recently gained prominence for their potential to high damage tolerance, isotropy, and imperfection insensitivity. However, the advantages of such irregular materials remain debated. Here, inspired by a pioneering stochastic growth rule, we present an irregular growth strategy that uses a limited set of cells to engineer mechanical stealth, achieving both static cloaking and camouflage within a narrow error tolerance—a feat challenging for periodic designs. Our approach demonstrates adaptability to diverse boundary loads and void configurations, ensuring reliable performance without requiring extensive datasets. Arbitrary cloaks generated with minimal samples retain camouflage under varied conditions, including mutual camouflage between targets with disparate void shapes. This framework is further extended to three-dimensional scenarios, highlighting its potential for cloaking and camouflage across multiscale applications.