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13 results for “Digital image Correlation”
Digital image correlation measurement of linear elastic steel specimen
<p>The dataset comprises the axial and lateral displacements on the surface of a plate with a hole subjected to tensile load. The displacement data are measured by digital image correlation and the material is assumed to behave linear elastic. The material under investigation is a common low-carbon steel alloy of type S235. The displacement data are used for calibration of a linear elastic constitutive model using parametric physics-informed neural networks and finite elements. For that purpose, the dataset comprises both the raw experimental displacement data and displacement data interpolated onto a regular grid using linear interpolation, where the interpolation routine is provided as well.</p>
Dataset for the publication "Identification of plasticity-induced crack closure by using high-resolution digital image correlation"
<p>This repository publishes the data generated in the article "Identification of plasticity-induced crack closure by using high-resolution digital image correlation" (see arxiv preprint <a href="https://arxiv.org/html/2409.02560v1">Plasticity-induced crack closure identification during fatigue crack growth in AA2024-T3 by using high-resolution digital image correlation (arxiv.org)</a>)</p> <p>This repository is structured with the following subfolders:</p> <ul> <li><strong>0_fe_data: </strong>contains the displacement field of the free surface of the 3D finite element model that were used to determine the crack opening curves and, in the following, the crack opening value Kop</li> <li><strong>1_hrdic_data: </strong>contains the high-resolution DIC displacement field data at a crack length of 27.8 mm at different load levels, starting from minimum load 1.5 kN to maximum load 15 kN</li> </ul>
Digital Image Correlation Workshop at the University of Manchester, September 2016
<p>Enabling Process Technologies sponsored a Digital Image Correlation (DIC) workshop on 8 September, 2016 at the School of Mechanical, Aerospace and Civil Engineering at the University of Manchester. Alistair Tofts, Director of Sales & Marketing at Correlated Solutions attended and provided a series of presentations covering DIC theory, and the working principles of the VIC lineup of DIC software.</p> <p>Afterwards, three discrete tests were conducted with the same specimen and speckle on three different frames and setups:</p> <ul> <li>High speed: Zwick HTM-5030 test frame, Photron SA.1 high speed cameras</li> <li>Quasi-static 2D: Instron 5969 test frame, Point Grey Research machine vision camera</li> <li>Quasi-static 3D: Instron 5659 test frame, 2 Point Grey Research machine vision cameras</li> </ul> <p>The archives included in this dataset are:</p> <ul> <li>Presentations: contains the slides from the presentations given by Alistair Tofts, as well as an outline/agenda given by Matthew Roy.</li> <li>3D_fast, 2D_slow and 3D_slow: correspond to the tests/setup described above, including all calibration and test images, along with VIC metadata for replaying analyses.</li> </ul> <p>Specimen geometry is shown on slide 6 of ~/Presentation/DICWorkshopPresSept2016_MJR.pptx, and markdown readme files are included to describe the test and data.</p>
Residual strains estimations in the annulus fibrosus through digital image correlation
<p>This upload correspond to the experimental data and all the python and LMGC software pipeline to process the data.</p> <p>The raw data correspond to images of the stress/strain relaxation process within the annulus fibrosus following a radial cut.</p> <p>The corresponding scientific article has been published in the following Diamond Open Access Journal : Journal of Theoretical, Computational and Applied Mechanics.</p>
Full-field displacements and strains obtained by digital image correlation during fatigue crack growth experiments
<p>This data publication contains full-field displacements and strains obtained by 3D digital image correlation (DIC) using a GOM Aramis 12M system including three fatigue crack propagation (fcp) experiments of AA2024-T3 aluminium sheet material.</p> <p>The repository consists of three datasets of different experiments named </p> <ul> <li>S<sub>950,1.6 </sub>- MT950 specimen, 1.6 mm sheet thickness, load ratios R=0.3, 1.0</li> <li>S<sub>160,2.0 </sub>- MT160 specimen, 2.0 mm sheet thickness, load ratios R=0.1, 0.25, 0.5, 0.75, 1.0</li> <li>S<sub>160,4.7 </sub>- MT160 specimen, 4.7 mm sheet thickness, load ratios R=0.1, 0.25, 0.5, 0.75, 1.0</li> </ul> <p>where S<sub>w,t</sub> denotes a middle tension (MT) specimen with width w and thickness t. The nodal DIC measurements at different times during the experiments are provided as .txt files we call <em>"nodemaps" </em>and stored in subfolders "<strong>Nodemaps</strong>". Each <em>nodemap</em> consists of a header containing meta data information like a running number (current stage index) or the applied force, followed by the nodal displacements and strains in tabular form. Additionally, the dataset S<sub>160,4.7</sub> contains crack path and crack tip labels for each nodemap in the subfolder "<strong>GroundTruth</strong>". The ground truth is provided as arrays of size 256x256. Each pixel of the array contains the label "2" for the class "crack tip", "1" for the class "crack path", or "0" for the class "background". These labels were created in a semi-manual fashion and can be used for machine learned crack detection using supervised training.</p> <p>These datasets were recently used to evaluate neural attention of convolutional neural networks trained on fatigue crack tip detection in <a href="https://www.nature.com/articles/s41598-022-13275-1">Melching et al. (Sci Rep, 2022)</a>. Additional guidance on data loading and usage can also be found at <a href="https://github.com/dlr-wf/explainable-crack-tip-detection">https://github.com/dlr-wf/explainable-crack-tip-detection</a>.</p> <p>The experiments S<sub>160,2.0</sub> and S<sub>160,4.7 </sub>were conducted and analysed by <a href="https://doi.org/10.1111/ffe.13433">Strohmann et al. (FFEMS, 2021)</a>.</p> <p>The experiment S<sub>950,1.6</sub> was conducted and analysed by <a href="https://doi.org/10.1111/ffe.13335">Breitbarth et al. (FFEMS, 2020)</a>.</p>
Digital Image Correlation for the Structural Helth Monitoring of composite GFRP materials
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Laboratory visualization of fault asymmetry formation via acoustic emission and digital imaging correlation
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Digital image correlation displacements and strains around a growing fatigue crack in an AA2024-T3 aluminium alloy
<p>This repository contains the data used in the research article:</p> <p>Strohmann, Melching, Paysan, Dietrich, Requena, Breitbarth. Next generation fatigue crack growth experiments of aerospace materials. <em>Scientific Reports</em>, 2024, <a href="https://doi.org/10.1038/s41598-024-63915-x" target="_blank" rel="noopener">https://doi.org/10.1038/s41598-024-63915-x</a>.</p> <p> </p> <p><strong>General Description</strong><br>The dataset contains digital image correlation (DIC) data of a growing fatigue crack in an AA2024-T3 alloy. For the experiment, two independent DIC measurement devices were used – a global full-field DIC and a local microscopic DIC. Thus, the dataset consists of two main directories. One for the 3D DIC data ("global_3d_dic") and a second one for the 2D microscopic DIC data ("local_2d_dic"). Both of these are described and connected by rich metadata.<br>The 3D DIC data directory contains two subdirectories (a "Nodemaps" directory and a "Connections" directory). Both of them contain 797 '.txt'-files. The "Nodemaps" give the DIC results (i.e. coordinates, displacement and strains) for every timestep throughout the experiment. These are usually maximum, minimum, and mean load of a certain load cycle. However, for few crack lengths, we obtained DIC data for a higher number (ca. 100) of images within one load cycle. The nodemaps' format and structure is optimized for data processing in the open-source Python package <a title="CrackPy" href="https://doi.org/10.5281/zenodo.10990494" target="_blank" rel="noopener">CrackPy</a>. The last integer number of each filename can be interpreted as a 'timestep' throughout the experiment. The "Connection" files represent the connections of the DIC facet center coordinates. These are necessary to export the "Nodemap" data to any mesh like dataset, e.g. for VTK. <br>The 2D microscopic DIC directory contains 3 subdirectories ("80", "90", "95") for predefined positions with respect to the specimen coordinate system. For each location, a number of DIC data are stored, again within two subdirectories "Nodemaps" and "Connections" as '.txt'-files. For the 2D DIC data, the last integer of each name cannot be correlated to a timestep. Instead, we provide a descriptive file "local_2d_microscopic_coordinates_by_nodemaps.csv" linking each and every "Nodemap"-file to its respective coordinates and timestep (i.e. the load cycles).</p> <p>To describe the data, we distinguish between<br>1. Higher-level metadata - these data contain information about the experiment and material. The data do not change between timesteps and are given within this description.<br>2. Timestep metadata - these data contain information about one timestep of the experiment and are stored in the header of each "Nodemap"-file.</p> <p> </p> <p><strong>Higher-level Metadata</strong><br>The experiment is described in detail in the reference publication by <a title="Strohmann et al. (2024)" href="https://www.researchsquare.com/article/rs-3128435/v1" target="_blank" rel="noopener">Strohmann et al. (2024)</a> and a summary is given below. Moreover, we provide a dictionary in javascript object notation explaining terms which are used in the higher-level metadata. We use such a dictionary since no standardized ontology is currently available. This dictionary is stored in the main directory as "higher_level_metadata_dictionary.json".</p> <p><em>Material </em><br>A commercially available AA2024-T3 aluminum alloy was tested in L-T orientation, i.e. rolling direction, L, parallel to the load axis. The specimen had a width W = 160 mm cut from a rolled sheet of 2 mm.</p> <p><em>Digital image correlation</em><br>For 3D DIC, we used a GOM Aramis 12M system with a facet size of 20 x 20 pixels and a 16 pixels facet distance. One facet, therefore, covers ~0.614 x 0.614 mm². For the 2D microscopic DIC we captured images using a Zeiss STEMI 206C light optical microscope (LOM), equipped with a Basler a2A5320-23µmPro global shutter CMOS camera. One image has a size of 10.2 x 5.7 mm², 5328 x 3040 Pixels and a facet size of 40x40 pixels (distance of facet center points 30 pixels). The LOM was mounted to a robotic arm, a KUKA lbr Iiwa Cobot.</p> <p><em>Fatigue crack growth</em><br>We used a standard uniaxial servo-hydraulic testing rig. We applied a cyclic load ranging from Fmin = 4.5 kN to Fmax = 15 kN, i.e. R=Fmin/Fmax = 0.3. Throughout the experiment, we measured the crack length using direct current potential drop (DCPD).</p> <p><em>Image acquisition during fatigue crack growth</em><br>We acquired reference images for the DIC calculations before the experiment. For the global DIC, this is simply an image of the unloaded specimen. For the local microscopic DIC, the reference images are acquired in a checker board pattern with an overlap of 70 %. The depth of focus was calibrated for each image individually following (see <a title="Paysan et al. (2023)" href="https://doi.org/10.1007/s11340-023-00964-9" target="_blank" rel="noopener">Paysan et al. (2023)</a>). Images were acquired every 0.5 mm of crack extension at minimum, maximum and 0.5(Fmax- Fmin).</p> <p> </p> <p><strong>Timestep Metadata</strong><br>The timestep-wise metadata is stored in the individual DIC output files, "Nodemaps". We explain the terms used in a second dictionary, "timestep_level_metadata_dictionary.json". For all DIC data, we stored all data coming from the machine controller, i.e. number of cycles, force, displacement of the cylinder and also potential and crack length calculated from the potential as well as current values for back face strain gauges at both back faces of the MT specimen. In addition, for the local microscopic DIC data, we also store the current location of the center point of the image with respect to the global coordinate system provided by the current position of the robot carrying the LOM.</p>
Experimental dataset: stationary images for digital image correlation uncertainty quantification
<div>----------------------------------------------------------------------------------------------------------------------------------------------------------------------</div> <div>-------------------------------------------------------------------------------- <strong>SUMMARY</strong> ---------------------------------------------------------------------------</div> <div>----------------------------------------------------------------------------------------------------------------------------------------------------------------------</div> <div> </div> <div>Stereo-DIC 5 MPx system was used to capture sets of stationary images for quantification of DIC uncertainties.</div> <div> </div> <div>----------------------------------------------------------------------------------------------------------------------------------------------------------------------</div> <div>-------------------------------------------------------------------------------- <strong>FOLDERS </strong>---------------------------------------------------------------------------</div> <div>----------------------------------------------------------------------------------------------------------------------------------------------------------------------</div> <div> </div> <div><strong>Image sets:</strong> </div> <div> </div> <div><strong>Set 1: </strong>100 stationary images with cross polarisation to reduce effect of specular reflection. Test sample clamped in the clamps of a uniaxial tensile test bench.</div> <div><strong>Set 2: </strong>Same as set 1, but test sample unclamped at the bottom, displaced by 1 mm vertically. Meant to introduce rigid body motion into teh stationary images. </div> <div>For investigation of the impact of cross-polarisation: image gradients made similar as much as possible by adjusting exposure time and apetrture. </div> <div><strong>Set 3:</strong> With cross polarisation - 100 stationary images.</div> <div><strong>Set 4:</strong> Without cross polarisation - 100 stationary images.</div> <div> </div> <div>Images for stereo calibration:</div> <div> </div> <div><strong>Calib_sets_1_2: </strong>Calibration images for sets 1 and 2 mentioned above </div> <div><strong>Calib_sets_3:</strong> Calibration images for set 3 mentioned above </div> <div><strong>Calib_sets_4: </strong>Calibration images for set 4 mentioned above </div> <div> </div> <div>----------------------------------------------------------------------------------------------------------------------------------------------------------------------</div> <div>------------------------------------------------------------------------ <strong>SUPPORTING NINFORMATION </strong>--------------------------------------------------------------------</div> <div>---------------------------------------------------------------------------------------------------------------------------------------------------------------------- </div> <div> </div> <div>Image folder for each set contains an *.xaml file with image capture settings.</div> <div>Each calibration image folder contains a *.caldat file with intrinsic and extrinsic stereo camera parameters identified by MatchID 2024.2 DIC package.</div>
Digital image correlation experiments involving in-plane loading of a composite laminate
<div>----------------------------------------------------------------------------------------------------------------------------------------------------------------------</div> <div>-------------------------------------------------------------------------------- <strong>SUMMARY</strong> ---------------------------------------------------------------------------</div> <div>----------------------------------------------------------------------------------------------------------------------------------------------------------------------</div> <div> </div> <div>Stereo-DIC 5 MPx system was used to perform experiments on a composite laminate.</div> <div>Thickness = 2.48 mm</div> <div>Layup is [0, 45 -45 90]2s leading to quasi-isotropic behaviour</div> <div> </div> <div>----------------------------------------------------------------------------------------------------------------------------------------------------------------------</div> <div>-------------------------------------------------------------------------------- <strong>FOLDERS </strong>---------------------------------------------------------------------------</div> <div>----------------------------------------------------------------------------------------------------------------------------------------------------------------------</div> <div> </div> <div><strong>Image sets: </strong> </div> <div><strong>Sample 1:</strong> monotonically loaded up to 7.2 kN. <br><strong>Sample 2: </strong>monotonically loaded up to 10 kN.<br><strong>Sample 3:</strong> loaded cyclically with increasing peak load to study the presence of permanent strains.</div> <div> </div> <div><strong>Each folder contains the following subfolders:</strong></div> <div><strong>stationary: </strong>stationary images for DIC noise evaluation</div> <div><strong>calib: </strong>calibration target images for stereo-DIC calibration using MatchID software</div> <div><strong>test: </strong>images of the test sample being loaded, with force.csv including load reading from the uniaxial test bench synced with the images. </div> <div> </div> <div> </div> <div>----------------------------------------------------------------------------------------------------------------------------------------------------------------------</div> <div>------------------------------------------------------------------------ <strong>SUPPORTING NINFORMATION </strong>--------------------------------------------------------------------</div> <div>---------------------------------------------------------------------------------------------------------------------------------------------------------------------- </div> <div> </div> <div>Each calibration image folder contains a *.caldat file with intrinsic and extrinsic stereo camera parameters identified by MatchID 2024.2 DIC package.</div>
Digital Image Correlation in Right Ventricular Evaluation
ClinicalTrials.gov study NCT03115294. IPD Sharing: NO. Countries: 1. Publications: 2.
Digital image correlation (DIC) measurement of contact stiffness
<p>Measurements with digital image correlation of normal and tangential contact stiffness for ground Ti-6Al-4V interfaces suggest a linear relationship between normal contact stiffness and normal load and a linear relationship between tangential contact stiffness and tangential load. The normal contact stiffness for these surfaces is observed approximately to be inversely proportional to an equivalent surface roughness parameter, defined for two surfaces in contact. The ratio of the tangential contact stiffness to the normal contact stiffness at beginning of a load step is seen to be given approximately by the Mindlin ratio. A simple empirical model is proposed to estimate both normal and tangential contact stiffness at different loads for ground Ti-6Al-4V surfaces of known surface roughness and coefficient of friction.</p>
Digital image correlation (DIC) measurement of contact stiffness
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