Parameter optimization of bidirectional re-entrant auxetic honeycomb metamaterial based on genetic algorithm
Source abstract
Abstract
Abstract In this work, we design and test a parameter optimization method by Python script to meet the urgent demand for lightweight honeycomb metamaterial. The method mainly focuses on the selection of parameters according to the mass and Poisson’s ratio of the honeycomb metamaterial. The bidirectional re-entrant honeycomb is proposed as an objective to be optimized and the formula of Poisson's ratio is deduced theoretically to establish the internal relation. Besides, the accuracy of the Python script results is verified by static compression experimental results and theoretical results. Combined the Python script programming model with the genetic algorithm optimization method, the optimal honeycomb metamaterial solutions are obtained. Results show that the parameter optimization method using multi-island genetic algorithm (GA) can avoid a local solution’s appearance, and both the shell model and the solid model can obtain the ideal optimal solution. Furthermore, the 3D honeycomb has an admirable auxetic effect according to the optimized parameters, which provides a piece of strong evidence for the continuous application of optimization algorithms to improve the mechanical properties of honeycomb metamaterial.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Cellular and Composite Structures · Advanced Materials and Mechanics · Fluid Dynamics Simulations and Interactions
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (42)
Show source-provided references
1. Poisson's ratio in skin
2. The use of auxetic materials in tissue engineering10.1039/C9BM01928F ↗
3. Properties and an anisotropic model of cancellous bone from the proximal tibial epiphysis10.1115/1.3138303 ↗
4. Mechanical characterization of skin-finite deformations10.1016/0021-9290(70)90055-2 ↗
5. Programmable acoustic metasurfaces10.1002/adfm.201808489 ↗
6. Acoustic wave science realized by metamaterials10.1186/s40580-017-0097-y ↗
7. Acoustic imaging with metamaterial luneburg lenses10.1038/s41598-018-34581-7 ↗
8. Adaptive genetic algorithm for optical metasurfaces design10.1038/s41598-018-29275-z ↗
9. Metamaterial lensing devices10.3390/molecules24132460 ↗
10. Meta-chirality: fundamentals, construction and applications10.3390/nano7050116 ↗
11. Chalcogenide phase change material for active terahertz photonics10.1002/adma.201808157 ↗
12. Designing hierarchical metamaterials by topology analysis with tailored Poisson’s ratio and Young’s modulus10.1016/j.compstruct.2019.01.076 ↗
13. Morphological optimization of tensegrity-type metamaterials10.1016/j.compositesb.2016.10.017 ↗
14. 1D to 3D multi-stable architected materials with zero Poisson's ratio and controllable thermal expansion10.1016/j.matdes.2019.108430 ↗
15. Mechanical response of negative stiffness truncated-conical shell systems: experiment, numerical simulation and empirical model10.1016/j.compositesb.2020.107898 ↗
16. Prediction network of metamaterial with split ring resonator based on deep learning10.1186/s11671-020-03319-8 ↗
17. Deep-learning-enabled on-demand design of chiral metamaterials10.1021/acsnano.8b03569 ↗
18. Bayesian machine learning in metamaterial design: fragile becomes supercompressible10.1002/adma.201904845 ↗
19. Rotation spring: Rotation symmetric compression-torsion conversion structure with high space utilization10.1016/j.compstruct.2020.112341 ↗
20. Metamaterial electromagnetic wave absorbers
21. A metamaterial analog of the Ising model10.1002/adma.201804210 ↗
22. Topology optimized architectures with programmable Poisson’s ratio over large deformations10.1002/adma.201502485 ↗
23. Zero Poisson's ratio cellular structure for two-dimensional morphing applications10.1016/j.compstruct.2015.08.048 ↗
24. Plasmonic nanoparticle simulations and inverse design using machine learning10.1039/C9NR03450A ↗
25. Programming shape using kirigami tessellations10.1038/s41563-019-0452-y ↗
26. A machine learning-based method to design modular metamaterials10.1016/j.eml.2020.100657 ↗
27. Learned integrated sensing pipeline: reconfigurable metasurface transceivers as trainable physical layer in an artificial neural network10.1002/advs.201901913 ↗
28. Mechanical properties of 3D re-entrant auxetic cellular structures10.1016/j.ijmecsci.2017.05.048 ↗
29. Fabrication and mechanical properties of CFRP composite three-dimensional double-arrow-head auxetic structures10.1016/j.compscitech.2018.05.014 ↗
30. A numerical study of auxetic composite panels under blast loadings10.1016/j.compstruct.2015.09.038 ↗
31. 3D compression–torsion cubic mechanical metamaterial with double inclined rods10.1016/j.eml.2020.100706 ↗
32. Compression spin bio-inspired arm: A conceptual model based on compression–torsion cubic mechanical metamaterials with variable cross-section10.1016/j.eml.2020.101069 ↗
33. Mechanical properties of 3D double-U auxetic structures10.1016/j.ijsolstr.2019.07.007 ↗
34. Elastic properties of two novel auxetic 3D cellular structures10.1016/j.ijsolstr.2017.05.031 ↗
35. Axisymmetric auxetics10.1016/j.compstruct.2018.07.116 ↗
36. Effect of auxetic structures on crash behavior of cylindrical tube10.1016/j.compstruct.2018.10.068 ↗
37. Positive, zero and negative Poisson’s ratio non-stochastic metallic cellular solids: Dependence between static and dynamic mechanical properties10.1016/j.compstruct.2019.111239 ↗
38. Shape optimization of bowtie-shaped auxetic structures using beam theory10.1016/j.compstruct.2019.111020 ↗
39. Metamaterials with negative Poisson’s ratio and non-positive thermal expansion10.1016/j.compstruct.2016.11.056 ↗
40. Indirect additive manufacturing based casting of a periodic 3D cellular metal-Flow simulation of molten aluminum alloy10.1016/j.jmapro.2014.11.001 ↗
41. Design of materials with prescribed nonlinear properties10.1016/j.jmps.2014.05.003 ↗
42. In-plane elasticity of a multi re-entrant auxetic honeycomb10.1016/j.compstruct.2017.08.014 ↗