Abstract
Recent advancements in manufacturing, finite element analysis (FEA), and optimization techniques have expanded the design possibilities for metamaterials, including isotropic and auxetic structures, known for applications like energy absorption due to their unique deformation mechanism and consistent behavior under varying loads. However, achieving simultaneous control of multiple properties, such as optimal isotropic and auxetic characteristics, remains challenging. This paper introduces a systematic design approach that combines modeling, FEA, genetic algorithm, and optimization to create tailored mechanical behavior in metamaterials. Through strategically arranging 8 distinct neither isotropic nor auxetic unit cell states, the stiffness tensor in a 5 × 5 × 5 cubic symmetric lattice structure is controlled. Employing the NSGA-II genetic algorithm and automated modeling, we yield metamaterial lattice structures possessing both desired isotropic and auxetic properties. Multiphoton lithography fabrication and experimental characterization of the optimized metamaterial highlights a practical real-world use and confirms the close correlation between theoretical and experimental data.
| Original language | English |
|---|---|
| Article number | 3 |
| Journal | npj Computational Materials |
| Volume | 10 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - Dec 2024 |
Bibliographical note
Publisher Copyright:© 2024, The Author(s).
Funding
Support to this work by the US National Science Foundation under grant 2134534 and 2124826 is gratefully acknowledged. B. B acknowledges support from the NSF Graduate Research Fellowship (DGE 2146752). SEM images were obtained using the Scios 2 DualBeam, and HIM images were acquired using the Zeiss ORION NanoFab, which are available at the Biomolecular Nanotechnology Center of the California Institute for Quantitative Biosciences, UC Berkeley. We extend our sincere appreciation to Prof. Hosemann for generously granting us access to the PI-87 Picoindenter and to Frances Allen for her invaluable guidance and expertise in HIM imaging. We also acknowledge Prof. Papadopoulos for providing guidance on FEA simulation and mechanical analysis, as well as Prof. Koumoutsakos and P. Weber for their valuable input on the optimization scheme. Support to this work by the US National Science Foundation under grant 2134534 and 2124826 is gratefully acknowledged. B. B acknowledges support from the NSF Graduate Research Fellowship (DGE 2146752). SEM images were obtained using the Scios 2 DualBeam, and HIM images were acquired using the Zeiss ORION NanoFab, which are available at the Biomolecular Nanotechnology Center of the California Institute for Quantitative Biosciences, UC Berkeley. We extend our sincere appreciation to Prof. Hosemann for generously granting us access to the PI-87 Picoindenter and to Frances Allen for her invaluable guidance and expertise in HIM imaging. We also acknowledge Prof. Papadopoulos for providing guidance on FEA simulation and mechanical analysis, as well as Prof. Koumoutsakos and P. Weber for their valuable input on the optimization scheme.
| Funders | Funder number |
|---|---|
| National Science Foundation | 2124826, DGE 2146752, 2134534 |
| University of California Berkeley | |
| California Institute for Quantitative Biosciences |