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<p>Plate lattice metamaterials offer higher strength-to-weight efficiency than traditional truss lattices. Their optimization, however, is challenging when having more parameters and multiple objectives. Here, we present an experimental mechanician (ExMech) to identify lightweight and high-strength plate lattices. ExMech integrates a robotic platform for standardized data generation and a multi-objective active learning framework for guided exploration of structure–property relationships. It optimizes plate–truss hybrid lattices in a three-variable, three-objective space. It establishes the Pareto front in 25 iterations, reducing experimental workload significantly. Pareto-optimal combinations show 15.6% and 12.0% improvements in compressive and shear yield strength respectively without compromising lightweight metrics. When prioritizing strength, it yields 158.5% and 194.8% improvements with a 13.2% increase in weight. By analyzing variable-objective relationships and failure patterns, we could identify more structurally efficient combinations. The discovered lattices are demonstrated in compression–shear tunable 3D-printed midsoles. This study illustrates a self-optimizing experimentation approach for plate lattice discovery, accelerating the discovery of new mechanical metamaterials.</p>

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Experimental mechanician for plate lattice metamaterial discovery

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月14日

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  • Songtao Hu,
  • Haoran Li,
  • Wenhui Lu,
  • Tian Xie,
  • Xijia Ding,
  • Xiantong Zhang,
  • Xi Shi,
  • Zhike Peng,
  • Xiaobao Cao

摘要

Plate lattice metamaterials offer higher strength-to-weight efficiency than traditional truss lattices. Their optimization, however, is challenging when having more parameters and multiple objectives. Here, we present an experimental mechanician (ExMech) to identify lightweight and high-strength plate lattices. ExMech integrates a robotic platform for standardized data generation and a multi-objective active learning framework for guided exploration of structure–property relationships. It optimizes plate–truss hybrid lattices in a three-variable, three-objective space. It establishes the Pareto front in 25 iterations, reducing experimental workload significantly. Pareto-optimal combinations show 15.6% and 12.0% improvements in compressive and shear yield strength respectively without compromising lightweight metrics. When prioritizing strength, it yields 158.5% and 194.8% improvements with a 13.2% increase in weight. By analyzing variable-objective relationships and failure patterns, we could identify more structurally efficient combinations. The discovered lattices are demonstrated in compression–shear tunable 3D-printed midsoles. This study illustrates a self-optimizing experimentation approach for plate lattice discovery, accelerating the discovery of new mechanical metamaterials.