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7 results for “fatigue crack growth”
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>
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>
High temperature properties incuding creep, creep crack growth rate and thermal fatigue linked with chemical composition of alloys derived from 1.4848 refractory stainless steels
<p>The following set of data results from cast alloys modifying chemical composition taking as reference 1.4848 alloy and getting sound samples that have been tested to calculate creep, creep crack growth rate and thermal fatigue data. </p>
Monte Carlo Simulations results for estimating an offshore structure fatigue life with a Fracture Mechanics based crack growth model, after additional information was considered through Bayesian inference at t=13 years
<p>Monte Carlo Simulations results for estimating an offshore structure fatigue life with a Fracture Mechanics based crack growth model, after additional information was considered through Bayesian inference at t=13 years</p>
Figure data for ``Fatigue crack growth under large scale yielding condition: a tool based on explicit crack growth''
<p>Figure data used for the paper "Fatigue crack growth under large scale yielding condition: a tool based on explicit crack growth" (doi 10.46298/jtcam.9296). The numerical values are provided as raw text in the tikz/pgfplots format. They can be compiled using latex/pdflatex engines. The name of each data file corresponds to the figure numbering in the paper.</p>
Multi-partner benchmark experiment of fatigue crack growth measurements from GDR 3651 FATACRACK
<p>The dataset presents a synthesis of an experimental benchmark performed in the context of the French national research network GDR FATACRACK . A sample has been designed to produce mixed-mode crack propagation and variation of small scale yielding conditions. Two geometries and two maximum load levels are defined for the two tested materials: a stainless steel and an aluminum alloy. Around ten participants performed experiments using their usual instrumentation. Among the eight possible parameter sets, three are selected for which detailed results are presented. A satisfying overall agreement is obtained. But, some discrepancies are evidenced due either to limitations of the instrumentation or simply because from one lab to the other the applied load is not exactly the same. The results obtained by all the participants as well as the description of their testing setups and measurement tools are collected in this dataset. It includes crack path, stress intensity factor range and crack growth rate measurement for the selected configurations. </p>
Fatigue Crack Growth in Aluminum Lap Joint
Fatigue experiments were conducted on aluminum lap-joint specimens, and lamb wave signals were recorded for each specimen at several time points (i.e., defined as number of cycles in fatigue testing). Signals from piezo actuator-receiver sensor pairs were reported and it was observed that these signals were directly related to the crack lengths developed during fatigue testing. Optical measurements of surface crack lengths are also provided as the ground truth. The data set is split in training and validation to facilitate the application of data-driven methods. This data set was generated at Arizona State University by Prof. Yongming Liu, Dr. Tishun Peng, and their collaborators. The data set was used for the Prognostics Health Management (PHM) Data Challenge for the 2019 Conference on Prognostics and Health Management. Other than the data set authors, the following individuals helped put together the 2019 PHM data challenge and make the data set publicly available: Matteo Corbetta and Portia Banerjee (KBR, Inc, NASA Ames), Kurt Doughty (Collins Aerospace), Kai Goebel (PARC), and Scott Clements (Lockheed Martin). Data Set Citation: Peng T, He J, Xiang Y, Liu Y, Saxena A, Celaya J, Goebel K. Probabilistic fatigue damage prognosis of lap joint using Bayesian updating. Journal of Intelligent Material Systems and Structures. 2015 May;26(8):965-79. Publication Citation: He J, Guan X, Peng T, Liu Y, Saxena A, Celaya J, Goebel K. A multi-feature integration method for fatigue crack detection and crack length estimation in riveted lap joints using Lamb waves. Smart Materials and Structures. 2013 Sep 4;22(10):105007.
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