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1,733 results for “Fatigue”
Datasets for Service-Like Creep-Fatigue Experiments on Grade P92 Steel
<p>The dataset contains experimental mechanical data from complex service-like creep-fatigue experiments performed isothermally at 620 °C and a low strain amplitude of 0.2 % on tempered martensite-ferritic grade P92 steel. The data sets in text file format provide cyclic deformation (min. and max. stresses) and the total (hysteresis) data of all recorded fatigue cycles for three different creep-fatigue experiments: 1) a standard relaxation fatigue (RF) test with symmetrical dwell times of three minutes introduced at minimum and maximum strain, 2) a fully strain-controlled service-like relaxation (SLR) test combining these three-minute peak strain dwells with a 30-minute dwell in between at zero strain, and 3) a partly stress-controlled service-like creep (SLC) test combining the three-minute peak strain dwells with 30-minute dwells at constant stress.</p> <p>Further information on data and data acquisition, analysis, and experimental details are given in “<em>Experimental Data from Service-Like Creep-Fatigue Experiments on Grade P92 Steel”, </em>submitted to <a href="https://www.sciencedirect.com/journal/data-in-brief">Data in Brief</a>. Additional analyses of these datasets, as well as experimental findings and discussions are presented in “<em>Creep-Fatigue of P92 in Service-Like Tests with Combined Stress- and Strain-Controlled Dwell Times</em>”, submitted to <a href="https://www.sciencedirect.com/journal/international-journal-of-fatigue">International Journal of Fatigue</a>.</p>
Data for "High-temperature low-cycle fatigue and fatigue-creep behaviour of Inconel 718 superalloy: Damage and deformation mechanisms"
<p>Title of dataset: Data for "High-temperature low-cycle fatigue and fatigue-creep behaviour of Inconel 718 superalloy: Damage and deformation mechanisms"<br>Name/institution/contact information: Dr. Michal Bartošák, Czech Technical University in Prague - Faculty of Mechanical Engineering, email: michal.bartosak@fs.cvut.cz<br>Date of data collection: The data were collected from the start of 2021 to the end of 2023.<br>File name structure: The data within the folder "SEM" are images of microstructural observations of selected specimens. The data within the folder "FATIGUE_LIFE" include the fatigue lifetimes, as well as the stress and strain amplitudes at mid-life, of all investigated specimens.</p> <p>See https://doi.org/10.1016/j.ijfatigue.2024.108369 for the associated article and a detailed description of the methods.</p>
DATA FOR VERY HIGH FREQUENCY INDUCED FATIGUE DAMAGE
<p>Dataset for publication in which you can find experimental data measured during the quasi-static and dynamic tensile tests and low and high-frequency fatigue tests, and figures describing the structural changes in the material due to the loading. Also, the files providing information about the experimental setup and conditions are coved by the dataset.</p>
Observations of microscale tensile fatigue damage mechanisms of composite materials for wind turbine blades
<p>A scout and zoom dataset including video-versions of the figures behind the following paper to where the references should be given:</p> <p>Mikkelsen, L.P. Observations of microscale tensile fatigue damage mechanisms of composite materials for wind turbine blades, IOP Conf. Series: Materials Science and Engineering <strong>380</strong> (2018) 012006 , http://iopscience.iop.org/article/10.1088/1757-899X/388/1/012006.</p> <p>The SFoV data-set is saved as both a 3D and a 2D (zipped) tiff stack.</p>
Evaluation of Selected Artificial Aging Protocols for Dental Composites Including Fatigue and Fracture Tests
<p>This research was funded by the National Science Centre, Poland [grant number: UMO-2020/37/N/ST5/00191, 2021].</p> <p>Datasets was basis for publication entitled: </p> <p>Evaluation of Selected Artificial Aging Protocols for Dental Composites Including Fatigue and Fracture Tests, published at Applied Sciences in 2024. </p> <p>The publication is a summary of the second step of the project, that aims standardizing an artificial aging protocol for dental composites. Until now, there has been no effort to create a standard protocol for evaluating the clinical performance of dental composites in a practical and efficient manner. Dental materials are not required to undergo studies that verify their durability over their expected lifespan before they are released to the market. Implementing a standardized aging process is essential to enhance the dental resin composite's ability to withstand oral conditions. Research in this area has been supported by the Preludium grant from the National Science Center in Poland.</p> <p>Three materials were tested to compare the degradation of resin from that of the filler and resin–filler interface. The first material (Resin F) was unfilled resin. The second and third materials were composites based on a similar resin matrix to Resin F but with differentiated fillers content. On the basis of the obtained data (https://zenodo.org/records/6583563), three protocols were selected for the second part of the project (standardization of artificial aging protocol for dental composites). The influence of three selected aging protocol on the material properties of the samples was determined based on flexural strength (FS), diametral tensile strength (DTS), Vickers hardness (HV) and microstructure evaluation. Additionally, fracture toughness (FT) and flexural fatigue limit (FFL) were determined. The proposed complex aging protocols should simulate prolonged, possibly several to even a dozen or more years of material usage in the oral cavity, thereby furnishing valuable insights into its prospective clinical performance in vitro. </p> <p>Authors hope to determine an artificial aging method for evaluating the clinical performance of dental composites. Our publication is first attempt to determine standard aging protocol for dental composites.</p>
Time-to-fatigue data for five Cypriniformes fish species and R script for data analysis
<p>The Excel file contains data from fixed velocity fatigue experiments for five small-sized Cypriniformes fish species. The recorded data includes common and scientific names of fish species, date and time of test trial, test flume length [cm], flow velocity treatment [cm/s], time-to-fatigue [sec], test water temperature [°C], fish mass [g], fish fork length [cm], fish width [cm], and fish height [cm]. The readme text file explains the column names used in the Excel file. The Rscript file contains the code used to analyse the data.</p>
Dataset for: "Fatigue assessment of structural components through the Effective Critical Plane factor"
<p>This dataset contains the <strong>Effective Critical Plane (ECP)</strong> post-processing of the classic fatigue experiments by Susmel & Taylor (2007, 2008) on En3B low-carbon steel specimens with holes and notches.<br>The re-analysis, carried out with the <strong>Fatemi–Socie (FS) variant</strong> of the ECP methodology, is presented in</p> <blockquote> <p>Chiocca A., Frendo F. “Fatigue assessment of structural components through the Effective Critical Plane factor,” <em>International Journal of Fatigue</em> 189 (2024) 108565. </p> </blockquote> <h3>1 File-naming convention</h3> <div> <div dir="ltr"><code><span><span><<span>CPvariant</span></span></span>>_<span><<span>SeriesID</span></span>>_<span><<span>LoadMode</span></span>>_<span><<span>Geometry</span></span>>_<span><<span>Size</span></span>>_R_<span><<span>LoadRatio</span></span>>[ _Phi<span><<span>PhaseDeg</span></span>> ].txt </code></div> </div> <div> <div> <table> <thead> <tr> <th>Token</th> <th>Meaning</th> <th>Examples</th> </tr> </thead> <tbody> <tr> <td><strong>CPvariant</strong></td> <td>Critical-plane parameter used: <code>FS</code> (Fatemi–Socie) or <code>SWT</code> (Smith-Watson-Topper)</td> <td><code>FS</code>, <code>SWT</code></td> </tr> <tr> <td><strong>SeriesID</strong></td> <td>Progressive integer that groups related tests</td> <td><code>1</code>, <code>2</code>, …</td> </tr> <tr> <td><strong>LoadMode</strong></td> <td>Type of loading</td> <td><code>Axial</code>, <code>Torsion</code>, <code>InPhase</code>, <code>OutPhase90</code></td> </tr> <tr> <td><strong>Geometry</strong></td> <td>Specimen geometry</td> <td><code>Hole</code>, <code>UNotch</code>, <code>VNotch</code></td> </tr> <tr> <td><strong>Size</strong></td> <td>Characteristic size in mm (decimal point → “_”)</td> <td><code>3_5</code> → 3.5 mm hole; <code>0_2</code> → 0.2 mm notch root</td> </tr> <tr> <td><strong>R <LoadRatio></strong></td> <td>Stress (or shear) ratio <em>R</em></td> <td><code>R_-1</code>, <code>R_0</code>, <code>R_0_1</code></td> </tr> <tr> <td><strong>_Phi<PhaseDeg></strong></td> <td>(Only for combined loading) axial–torsional phase shift</td> <td><code>_Phi90</code></td> </tr> </tbody> </table> <div> <div> </div> </div> </div> </div> <p><strong>Example</strong> – <code>FS_1_Axial_Hole_3_5_R_-1.txt</code> stores FS-ECP data for a 3.5 mm-diameter holed specimen under fully-reversed axial loading (<em>R</em> = -1).</p> <h3>2 Column definitions</h3> <ol> <li> <p><strong>Nf_exp</strong> – experimental number of cycles to failure</p> </li> <li> <p><strong>ECP_factor</strong> – Effective Critical Plane factor (FS or SWT)</p> </li> <li> <p><strong>Nf_pred</strong> – predicted number of cycles to failure from the calibrated ECP model</p> </li> </ol> <p>Units: stresses in the underlying calculations are in MPa; cycle counts are absolute.</p> <h3>3 Experimental campaign (summary)</h3> <ul> <li> <p><strong>Material:</strong> cold-rolled low-carbon steel En3B; chemical and mechanical properties are reported in Table 1 of the article. </p> </li> <li> <p><strong>Geometries:</strong> through-holes Ø 3.5 mm & Ø 8 mm, flat U-notch (radius 1.5 mm), flat V-notch (radius 0.12 mm), plus cylindrical V-notches with root radii 0.2 – 4 mm. </p> </li> <li> <p><strong>Loading conditions:</strong> pure axial, pure torsion, proportional axial–torsion (φ = 0°) and non-proportional axial–torsion (φ = 90°); stress ratios <em>R</em> = -1, 0, 0.1. </p> </li> <li> <p><strong>ECP settings:</strong> the optimal control radius for the FS factor is 0.20 mm, established by best-fitting V-notched and Ø 8 mm hole datasets. </p> </li> </ul> <h3>4 Re-use & reproducibility</h3> <ul> <li> <p>MATLAB scripts implementing the FS ECP algorithm are available at <strong><a href="https://github.com/achiocca1/ECP" target="_new" rel="noopener">https://github.com/achiocca1/ECP</a></strong> (MIT licence).</p> </li> <li> <p>Finite-element meshes can be shared on reasonable request to the corresponding author.</p> </li> <li> <p><strong>Please cite both this Zenodo record and the journal article</strong> when you use the data.</p> </li> </ul>
Data for "Using physics-informed neural networks to predict the lifetime of laser powder bed fusion processed 316L stainless steel under multiaxial low-cycle fatigue loading"
<p>Title of dataset: Data for "Using physics-informed neural networks to predict the lifetime of laser powder bed fusion processed 316L stainless steel under multiaxial low-cycle fatigue loading".</p> <p>Name/institution/contact information: Dr. Michal Bartošák, Czech Technical University in Prague - Faculty of Mechanical Engineering, email: michal.bartosak@fs.cvut.cz.</p> <p>Date of data collection: The data were collected between 2021 and 2024.</p> <p>File name structure: The data consists of two files: "316L_fatigue_and_defects.xls," which contains fatigue lifetime data and defect characteristics, and an associated description file, "read_me.txt."</p> <p>See "https://doi.org/10.1016/j.ijfatigue.2024.108608" for the associated article and a detailed description of the methods.</p>
Physiological Data Collected from smartwatch: EDA, Pulse Rate, and Skin Temperature for Stress and Fatigue Analysis
<p>The dataset contains multiple columns capturing both <strong>physiological and demographic data</strong>.<strong> Physiological data</strong>, collected using the <strong>Empatica EmbracePlus smartwatch,</strong> includes electrodermal activity (EDA), pulse rate, and skin temperature. These metrics provide insights into participants' stress and fatigue levels. Empatica's proprietary algorithms preprocess the raw data, extracting digital biomarkers and metrics that reflect the wearer's physiological and behavioral states. <strong>The processed data is aggregated on a per-minute basis.</strong></p> <p>Demographic information, such as age, gender, fitness level, and sleep duration from the previous night, is also included. Additionally, participants rated their perceived physical fatigue on the Borg scale (ranging from 6 to 20), offering a subjective measure of exertion during or after physical tasks.</p> <p>The dataset was collected during controlled simulations of industrial tasks in a fitness environment. These simulations involved repetitive activities, including weightlifting, resistance band exercises, and isometric tasks, designed to mimic the physical demands of industrial work. This approach allowed for the safe and effective study of physical fatigue. The resulting data provides valuable insights into the physiological responses associated with repetitive physical labor.</p>
Fatigue life of S960 high strength steel with laser cladded functional surface layers
<p>This dataset to paper: Fatigue life of S960 high strength steel with laser cladded functional surface layers, which includes mainly raw data for S-N curves.</p>
A Data-Driven Epigenetic Characterization of Morning Fatigue Severity in Oncology Patients Receiving Chemotherapy: Associations with Epigenetic Age Acceleration, Blood Cell Types, and Expression-Associated Methylation
<p>This dataset contains supplementary materials including the eCpG mapping analysis results and annotation. The manuscript has been accepted for publication at Cancer Medicine. Please cite both the paper as well as the DOI of this dataset if you make use of the data.</p>
Multi-channel Surface EMG Dataset for Fatigue analysis
<p>This is the data used in paper "Upper Limb Muscle Fatigue Analysis Using Multi-channel Surface EMG" DOI: 10.1109/NILES50944.2020.9257909</p> <p>Data can be found as a txt files for each subject separately or can be found as .mat file with all subjects included.</p> <p>Data details:</p> <ul> <li>Sampling Frequency= 200 Hz </li> <li>8-Bit resolution</li> <li>15 Healthy Subjects </li> <li>6 Kg Load with elbow flexed to a 90 angle</li> <li>120 Seconds Duration</li> <li>8 channels sEMG </li> <li>50 Hz Notch Filtered</li> </ul> <p>For more details and citation:</p> <p>A. Ebied, A. M. Awadallah, M. A. Abbass and Y. El-Sharkawy, "Upper Limb Muscle Fatigue Analysis Using Multi-channel Surface EMG," 2020 2nd Novel Intelligent and Leading Emerging Sciences Conference (NILES), 2020, pp. 423-427, doi: 10.1109/NILES50944.2020.9257909.</p>
Fatigue Crack Propagation Benchmark, GDR 3651 FATACRACK
<p>This is a data set for fatigue crack propagation following the benchmark defined within the french research network GDR 3651 FATACRACK funded by CNRS <a href="http://www.gdr3651.cnrs.fr/">http://www.gdr3651.cnrs.fr</a>. </p> <p>Only two test configurations are reported in this data set. But DIC allows to provide for the analysis not only of the crack tip state ( tip position, crack growth rate and stress intensity factors) but also of the displacement amplitude along the boundary of the analyzed domain. The data set can thus be used to validate fatigue crack growth models.</p> <p>A detailed description of the data set is given in the pdf file Fatigue_Crack_Propagation_Benchmark.pdf.</p> <p><strong>!!!!! there is unfortunately a mistake in the pdf document: the sample thickness is 4 mm !!!!!</strong></p> <p><br> </p> <p> </p>
Individualized mental fatigue does not impact neuromuscular function and exercise performance
<p>Previous work has shown that mental fatigue may have negative consequences on cognitive or physical performance, although recent reports question this previous empirical evidence. Here, we investigate the critical role of inter-individual differences in susceptibility to develop mental fatigue by measuring neurophysiological and physical responses to an individualized mental fatigue task. We expected mental load to alter both subjective, i.e., increased subjective perception of fatigue, and objective markers of fatigue, i.e., impaired knee extensor neuromuscular function, impaired corticospinal excitability and reduced cerebral oxygenation. Even though all participants performed a similar mental effort, their performance in a subsequent exercise did not differ. Furthermore, even if there was an elevated subjective feeling of mental fatigue, none of the neurophysiological parameters were affected. The study provides new insights into an issue that has grown in popularity in recent years without questioning individual differences and which has taken for granted the detrimental effect of acute mental fatigue on performance.</p>
Fracture and fatigue properties of AISI 316LN at cryogenic temperatures
<p>Dataset containing the fracture toughness and the fatigue properties (specifically, the Paris' law parameters) of the stainless steel AISI 316LN at 4 and 7 K. The data is collected from a literature review (the bibliography is provided in the document).</p>
Data from: Fatigue crack propagation in AA5083 structures additively manufactured via multi-layer friction surfacing
<p>This dataset contains the data for the publication " Fatigue crack propagation in AA5083 structures additively manufactured via multi-layer friction surfacing"</p>
Understanding Fatigue Through Biosignals: A Comprehensive Dataset
<p>Fatigue is a multifaceted construct, that represents an important part of human experience. The two main aspects of fatigue are the mental one and the physical one, that often intertwine, intensifying their collective impact on daily life and overall well-being.<br>To soften this impact, understanding and quantifying fatigue is crucial. Physiological data play a pivotal role in the comprehension of fatigue, allowing a precious insight into the level and type of fatigue experienced.</p> <p>The MePhy dataset includes physiological data gathered while inducing different types of fatigue conditions, in particular mental and physical fatigue. We collected various biosignals closely associated with fatigue (ECG, EDA, EMG and Eye Blinking). Test participants endured a four-part experiment that aimed to elicit mental fatigue, physical fatigue and a combination of both. </p> <p>The main folder contains:</p> <ul> <li>MePhy Dataset folder, which contains the dataset;</li> <li>ReadMe.pdf, which provides more informations about the dataset;</li> <li>Mental Fatigue Inducing Test folder, which includes the HTML application used to simulate mental fatigue in the test participants. </li> </ul> <p> </p> <p>A more in depth description of the MePhy dataset can be found in the following paper <a href="https://doi.org/10.1145/3610977.3637485">https://doi.org/10.1145/3610977.3637485</a>.</p> <p><strong><em>Marta Gabbi, Luca Cornia, Valeria Villani, and Lorenzo Sabattini (2024) Understanding Fatigue Through Biosignals: A Comprehensive Dataset. In Proceedings of the 2024 ACM/IEEE International Conference on Human-Robot Interaction (HRI ’24).</em></strong></p> <p> </p>
Quasi-Static and Fatigue Testing Dataset for soft bone cements according to ASTM F2118
<p>This Dataset features quasi-static and fatigue data of PMMA cements. All the tests were run on an MTS 858 Mini Bionix (MTS Systems Corporation, United States). In summary, this dataset contains:</p> <ul> <li>Videos captured for marker tracking used in a virtual extensometer (.mp4)</li> <li>Quasi-static testing data for the PMMA cements (.txt)</li> <li>Fatigue data for three different stress levels (5MPa, 7MPa, 9MPa) (.txt)</li> <li>An Excel sheet with corrected tensile properties (.xlsx)</li> </ul> <p>General Abbreviations:</p> <ul> <li>VS is the V-Steady Cement</li> <li>VSLA is the V-Steady Cement with 12%vol linoleic acid</li> </ul> <p>Abbreviations for Fatigue Data:</p> <ul> <li>B is the batch number</li> <li>S is the sample number</li> </ul>
Dataset from: Numerical simulation of fatigue crack growth in offshore mooring chains
<p>A polynomial solution for approximating the stress intensity factor (SIF) along the front of a semi-elliptical crack in a curved round bar has been developed. The normal stress distribution in the crack plane is assumed to be on a cubic polynomial form. Coefficients for the polynomial SIF solution have been calculated using least-squares fitting of finite element analysis results (<em>n</em>=4480 per stress distribution component). </p>
Effect of Hand Reflexology Massage on Fatigue and Anxiety Among Patients Undergoing Hemodialysis
ClinicalTrials.gov study NCT07253831. IPD Sharing: NO. Countries: 0. Publications: 2.
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OpenNeuro
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