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12 results for “mechanical metamaterials”
Computational Design of Multimodal Combinatorial Mechanical Metamaterials
<p>This dataset contains the data used to design multimodal mechanical metamaterials as described in the paper 'Prospecting for Pluripotency in Metamaterial Design', as published in Phys. Rev. Research 7(2), 023299.</p> <p>In this paper, the data is used to design 5×5 unit cells with desired deformation (zero) modes. The dataset contains the data used to train neural networks (CNN_data.zip), the designs generated by genetic algorithm (step_i.zip) and their mode structures (step_ii.zip), and the designs obtained through our design approach as described in the paper (step_ii.zip). Additionally, there is data comparing the efficiency of using a genetic algorithm or a hill climbing method to generate designs with a large number of intensive modes (step_i.zip).</p>
Supplementary codes and datasets for "Wang tiles enable combinatorial design and robot-assisted manufacturing of modular mechanical metamaterials"
<p>This repository provides data and codes for manuscript “Wang tiles enable combinatorial design and robot-assisted manufacturing of modular mechanical metamaterials” by M. Doškář, M. Somr, R. Hlůžek, J. Havelka, J. Novák, and J. Zeman, published first as a preprint <a href="https://arxiv.org/abs/2305.09280">arXiv:2305.09280</a> at arXiv.org; see the actual description of the Zenodo entry for the latest reference.</p> <p>This repository contains:</p> <ol> <li>MATLAB and C++ source codes for combinatorial design and numerical analyses (folder <code>./numerics/</code>),</li> <li>experimental data (folder <code>./experiments/</code>),</li> <li>3D models of parts used in robotic-assembly (folder <code>./models/</code>),</li> <li>a control script for robotic assembly (folder <code>./robotics/</code>).</li> </ol> <p><strong>Numerics</strong></p> <p>All simulations were performed with an in-house MATLAB code, which extends the finite element toolbox for finite strain calculations accompanying the work of <a href="https://doi.org/10.1016/j.cma.2020.113333">van Bree, S. E. H. M., Rokoš, O., Peerlings, R. H. J., Doškář, M., & Geers, M. G. D. (2020). A Newton solver for micromorphic computational homogenization enabling multiscale buckling analysis of pattern-transforming metamaterials. Computer Methods in Applied Mechanics and Engineering, 372, 113333</a>. In particular, this snapshot corresponds to a cleaned-up version (excluding files unrelated to the publications) of commit <code>21cfc2e9</code>.</p> <p>The MATLAB codebase contains MEX files written in C++ to accelerate selected procedures. In order to run any code, these MEX files must be compiled first. We use CMake build automation, with the main <code>CMakeLists.txt</code> located in <code>./numerics/mex</code>.</p> <p>Combinatorial search was performed by the <code>RUN_modular_exploration.m</code> script; see definition of problems with the script. The results of the enumerations, stored in <code>./dat/exploration</code>, were analysed with <code>POST_modular_S_v3.m</code>, identifying layouts leading to the extreme (min/max) tilt angles.</p> <p>Comparison against experimental measurements was facilitated by a series of scripts <code>POST_DIC_{...}.m</code>. First, run <code>POST_DIC_step1_extract_points_in_mesh.m</code> to identify locations.mat. Next, post-process extensometer data with <code>POST_DIC_step2_merge_extensometer_data.m</code>, and use <code>POST_DIC_step3_impose_extracted_BC.m</code> to parse DIC results in a format suitable for imposing BC later in this script. Finally, comparison between experimental and computed displacements is provided by <code>POST_DIC_step4_modular_comparison_experiments.m</code>. (Note that the particular files need to be manually provided in the “Compute deformation process” part of <code>POST_DIC_step4_modular_comparison_experiments.m</code>.)</p> <p><strong>Experimental data</strong></p> <p>This folder contains data from (i) an unixaial tension test of a dogbone specimen (both from a MTS loading machine and DIC data) and (ii) two measurement sessions extracting the L-shape domain responses using DIC (<code>20_11_30 - Hluzek_Elka_newassemblyplan</code> and <code>21_04_12 - Hluzek_ Elka_quarters</code> with lower loading threshold). For post-processing, see the above-mentioned <code>POST_DIC_{...}.m</code> scripts. <code>*.mat</code> files present directly in <code>./experiments/</code> folder were obtained and are need by those scripts.</p> <p><strong>3D models</strong></p> <p>The folder contains geometrical models for individual parts needed for robot-assisted assembly of module molds for casting. This includes:</p> <ol> <li>a silo extension to store more tiles (file <code>silo_extension.stl</code>),</li> <li>formwork modules around the main structure for the purpose of casting silicone (file <code>tile_formwork.stl</code>),</li> <li>all types of tiles for the inside structure (file <code>tile_inside_types.stl</code>),</li> <li>a spacer shaped for YuMi base to ensure correct distance of the silo and build plate (file <code>yumi_base_1.stl</code>),</li> <li>a spacer shaped for YuMi base to ensure correct distance of the silo and build plate (file <code>yumi_base_2.stl</code>),</li> <li>a spacer shaped for YuMi base to ensure correct distance of the silo and build plate (file <code>yumi_base_3.stl</code>),</li> <li>connection for spacers (file <code>yumi_base_4.stl</code>),</li> <li>spacer holding a silo and the build plate (file <code>yumi_base_5.stl</code>),</li> <li>YuMi grippers with extensions to hold the tiles (file <code>grippers_extend.st</code>).</li> </ol> <p><strong>Robotics</strong></p> <p>The folder contains a single file with a script created in RobotStudio (RobotWare Version: 6.08.01.00, SmartGripper Version: 3.55.0000.00) to assemble the plan with YuMi IRB 14000-0.5/0.5 left hand.</p> <p><strong>Acknowledgement</strong></p> <p>The related research, experiments, and code development were supported by the <a href="https://gacr.cz/en/">Czech Science Foundation</a>, project No. 19-26143X.</p>
The Extreme Mechanics of Viscoelastic Metamaterials
<p>This dataset contains all data as used for the paper 'The Extreme Mechanics of Viscoelastic Metamaterials'. It provides example files on viscoelastic modelling in Abaqus and on deriving viscoelastic material properties,</p> <p> </p> <p><strong>Abstract:</strong></p> <p>Mechanical metamaterials made of flexible building blocks can exhibit a plethora of extreme mechanical responses, such as negative elastic constants, shape-changes, programmability and memory. To date, dissipation has largely remained overlooked for such flexible metamaterials. As a matter of fact, extensive care has often been devoted in the constitutive materials’ choice to avoid strong dissipative effects. However, in an increasing number of scenarios, where metamaterials are loaded dynamically, dissipation can not be ignored. In this review, we show that the interplay between mechanical instabilities and viscoelasticity can be crucial and can be harnessed to obtain new functionalities. We first show that this interplay is key to understanding the dynamical behaviour of flexible dissipative metamaterials that use buckling and snapping as functional mechanisms. We further discuss the new opportunities that spatial patterning of viscoelastic properties offer for the design of mechanical metamaterials with properties that depend on loading rate.</p>
Supplementary data for study on "Superplastic 3D printed nitinol woven metamaterials lead to dramatic variations of mechanical properties by design"
<p>Raw and processed data from experimental compression testing of 3D printed nitinol lattices and wovens are provided as supplementary materials for the mentioned study, submitted for evaluation to the journal of Virtual and Physical Prototyping.</p>
DNA-silica nanolattices as mechanical metamaterials
<p>Mechanical metamaterials consist of intricate periodic structures made using additive manufacturing techniques. Yet, nanoscale (<10 nm) features cannot be directly printed using these fabrication techniques, although this is the size regime in which enhanced material properties appear. In addition, current additive manufacturing techniques cannot easily combine disparate materials (e.g., soft, biological polymers with hard ceramics) into structural motifs. Here, we show that DNA origami can be used to construct octahedral-based isotropic and anisotropic nanolattices. When coated with a thin silica layer, these nanolattices obtain strength and energy absorption competitive with the best additively manufactured nanolattices. The DNA nanolattices have strut 6 nm diameters and ~50 nm unit cells, which are two orders of magnitude smaller than lithography-based lattices. The silica coating is as thin as ~1.65 nm, which results in enhanced strength. Atom probe tomography confirms the nanoscale distribution of DNA and silica in the octahedral lattice geometry. Finite element modeling (FEM) reveals two dominant failure modes: buckling at lower coating thicknesses and tensile fracture at higher thicknesses. Molecular dynamics (MD) simulations reveal that the DNA delays failure by suppressing buckling within the lattice struts, while the nanoscale silica undergoes a surface buckling mode which contributes to increased strength at large strains.</p>
Mechanical Metamaterial: Square Array of Circular Holes Under Deformation
<p>This repository contains data for a research project involving graph neural networks (GNNs) applied to mechanical metamaterials and their deformations. </p> <p>The mechanical metamaterials of interest consist of a flexible rubber-like material with a square grid of almost-circular holes in it, of various diameters. The holes are not quite circular, because they were made slightly elliptic to avoid the bifurcation point.</p> <p>This data was used to obtain the results in the paper 'Similarity Equivariant Graph Neural Networks for Homogenization of Metamaterials'.</p> <p>The data includes PyTorch Geometric Graph objects, raw data from MATLAB simulations, and files describing the finite element mesh. The Jupyter notebook 'data_create_graphs.ipynb' in the GitHub repository can be used to turn the raw data and mesh information into the graph objects.</p> <p>GitHub repository with the code: <a title="https://github.com/FHendriks11/SimEGNN" href="https://github.com/FHendriks11/SimEGNN">https://github.com/FHendriks11/SimEGNN</a></p> <p>Link to the corresponding paper <em>Similarity Equivariant Graph Neural Networks for Homogenization of Metamaterials</em>: <a href="https://www.sciencedirect.com/science/article/pii/S0045782525001392">https://www.sciencedirect.com/science/article/pii/S0045782525001392</a> (also on ArXiv: <a title="https://arxiv.org/abs/2404.17365" href="https://arxiv.org/abs/2404.17365">https://arxiv.org/abs/2404.17365</a>)</p> <p>The .pkl files are pickle files and can be opened in Python using the standard pickle library. The mesh files, which have the extension .mat, are MatLab files and can be opened either in MatLab or in Python using scipy.io.loadmat from the scipy library.</p>
Dataset and Code for the article "Metamaterial-enabled asymmetric negative refraction of GHz mechanical waves"
<p>This submission contains scripts for generating ab-initio and/or from raw data the figures of the article<br> "Metamaterial-enabled asymmetric negative refraction of GHz mechanical waves" by SIMONE ZANOTTO,<br> GIORGIO BIASIOL, PAULO VENTURA SANTOS, AND ALESSANDRO PITANTI, published in NATURE COMMUNICATIONS, 2022.</p>
Attenuating surface gravity waves with mechanical metamaterials
<p>Videos showing simulations of one or more submerged oscillators attenuating surface gravity waves, related to the publication </p> <p><a href="https://aip.scitation.org/author/de+Vita%2C+F">F. De Vita</a><em>, </em><a href="https://aip.scitation.org/author/de+Lillo%2C+F">F. De Lillo</a><em>, </em><a href="https://aip.scitation.org/author/Bosia%2C+F">F. Bosia</a><em>, and </em><a href="https://aip.scitation.org/author/Onorato%2C+M">M. Onorato</a>, "Attenuating surface gravity waves with mechanical metamaterials", Physics of Fluids 33, 047113 (2021) <a href="https://doi.org/10.1063/5.0048613">https://doi.org/10.1063/5.0048613</a></p> <p>Also included are Data relative to Figs. 3, 5, 6, 8 and gnuplot scripts to generate the figures.</p>
Stiffness reprogrammable magnetorheological metamaterials inspired by the spine for multi-bit visual mechanical information processing
Open the record for dataset details and reuse information.
DNA-silica nanolattices as mechanical metamaterials
Open the record for dataset details and reuse information.
Counting and Sequential Information Processing in Mechanical Metamaterials
<p>This dataset contains images and driving protocols used in the paper: "Counting and Sequential Information Processing in Mechanical Metamaterials", published in Physical Review Letters.</p> <p>In this paper we demonstrate "beam counters"; metamaterials that count driving cycles. We demonstrate the counters sensitivity to various driving amplitudes and show how this might be used to infer more information from the applied driving and how to construct a "lock and key" metamaterial with an internal state that can only be reached with one unique input sequence.<br> <br> The data in this replication package is hierarchically organized by making use of a directory per figure in the paper. Contained in this replication package are a number of files used in the figure as described below.</p> <p>Experimental data from the measurement setup is stored in matched subdirectorys with a name, for example "001/" and a file "times_001.csv". Here the subdirectory contains images taken of the sample and the csv file contains the times at which the images were taken, the change in pixel intensity from one image to the next (sampled at a shorter interval than the saved images) and the position of the driving stage and a measured inductive position.<br> </p> <ul> <li>fig1 <ul> <li>001/<br> - directory containing the original unedited figures comparing above and below D*</li> <li>001_cropped_selection/<br> - directory containing a cropped selection of the images used in the figure</li> <li>times_001.csv</li> </ul> </li> <li>fig2 <ul> <li>002/<br> - directory containing images of the ten counter being compressed with marked m-beams</li> <li>times_002.csv</li> <li>Kymograph.tif<br> - A kymograph image calculated from 002 by filtering the colored m-beams and taking stacking a single horizontal slice from all of the images.</li> <li>kymograph.svg<br> - The plotted horizontal position of the beam traces visible in Kymograph.tif</li> </ul> </li> <li>fig3 <ul> <li>000/<br> - directory containing images of the compression of counter with uncut a-beams</li> <li>000_cropped/</li> <li>times_000.csv</li> <li>004/<br> - directory containing images of the same counter with the a-beams slit cut</li> <li>004_cropped/</li> <li>times_004.csv</li> <li>comparison/<br> - directory containing a selection of cropped images at comparable driving used in the figure</li> </ul> </li> <li>fig4 <ul> <li>Alternative starting condition/ <ul> <li>001/<br> - directory containing images of the ten-counter compressed in an alternative starting state</li> <li>001_cropped/<br> - directory with cropped versions of the images in 001/ and further cropped versions in further subdirectories</li> <li>001_selection/</li> <li>times_001.csv</li> </ul> </li> <li>BBAC machine <ul> <li>006/<br> - directory containing images of the four counters making up the BBAC machine under a driving of BAC</li> <li>006_state/<br> - selection of images used in the paper with a subdirectory contining cropped versions</li> <li>times_006.csv</li> </ul> </li> </ul> </li> </ul>
Datasets for "Automatic Design of Mechanical Metamaterial Actuators"
<p><strong>Publication</strong></p> <p>arXiv preprint: <a href="https://arxiv.org/abs/2002.03032">https://arxiv.org/abs/2002.03032</a></p> <p>journal version: [add link to journal]</p> <p> </p> <p><strong>Code</strong></p> <p><a href="https://github.com/ComplexityBiosystems/metamech">https://github.com/ComplexityBiosystems/metamech</a></p> <p><a href="https://github.com/complexitybiosystems/metamech_datasets">https://github.com/complexitybiosystems/metamech_datasets</a></p> <p> </p> <p><strong>Datasets</strong></p> <p>Figure 2:</p> <ul> <li><em>antiparallel_{human,machine}_design.stl</em>: STL files for human design and machine design configurations performing antiparallel movement.</li> </ul> <p>Figure 3: </p> <ul> <li><em>crane_scaling_dataframe.csv: </em>raw efficiency values and execution times used to generate Figure 3</li> </ul> <p>Figure 4:</p> <ul> <li><em>eta_random.dat</em>, <em>eta_triang.dat, plot_distrib.gnupot</em>: raw values of efficiency and gnuplot script used to generate Figure 4 panel d</li> <li><em><em>eta_vs_displ_random.dat</em>, <em>eta_vs_displ_triang.dat</em></em><em>, eta_vs_displ.gnuplot</em>: raw values of efficiency and gnuplot script used to generate Figure 4 panel c</li> <li><em>structures.tar.bz2</em>: all the structures relative to Figure 4 panel d, in a format suitable for visualization with ovito software</li> </ul> <p>Figure 5: data files for human and machine generated pairs of pliers in a format suitable for visualization with ovito software. "*.agr" and "*.data" files can be opened with any text editor to directly visualize the raw data. </p> <ul> <li><em>*_rest.data:</em> rest configurations which are represented in grey in Fig. 5a and Fig. 5b. </li> <li><em>*_F0.01.data</em>: displaced configurations with input force Fext = 0.01 and output spring stiffness kext = 0.01, also reported in Fig. 5a and Fig. 5b.</li> <li><em>*.agr</em>: dataset for Figure 5c and 5d in xmgrace format.</li> </ul> <p>Figure 6:</p> <ul> <li><em>Fig6/Fig6b/*.csv</em>: raw values of efficiency used to generate Figure 6 panel b</li> <li><em>Fig6/</em><em>Fig6c-training-data/*/*.png: </em>a total of 1.163.733 PNG images, of size 192 x 168 pixels, as described in the methods section of the manuscript, organized in directories corresponding to different runs.</li> <li><em>Fig6/Fig6c-training-data/metadata.csv: </em>maps each image path to its associated efficiency value</li> <li><em>Fig6/Fig6c-efficiency-data/eta_{CNN,DEM}.txt</em>: raw values of efficiency used to generate Figure 6 panel c.</li> </ul> <p>Figure 7:</p> <ul> <li><em>Fig7/transfer_learning_regression_values.csv: full raw data, including regression coefficients, p-values, slopes and intercepts corresponding to Figure 7 panels i and j</em></li> </ul> <p><em>Figure S3:</em></p> <ul> <li><em>orthogonal_{human,machine}_design.stl: STL files for human design and machine design configurations performing orthogonal movement.</em></li> </ul> <p> </p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.