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12 results for “Fatigue Prediction”
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>
Supplemental Material to Doctoral Thesis "Engineering approach for predicting tunneling crack initiation in trailing-edge adhesive joints of wind turbine blades under mechanical fatigue and thermal residual stresses"
<p>This set supplements the figure data to the doctoral thesis "Engineering approach for predicting tunneling crack initiation in trailing-edge adhesive joints of wind turbine blades under mechanical fatigue and thermal residual stresses", DOI: <a href="https://doi.org/10.14279/depositonce-19144">10.14279/depositonce-19144</a></p>
Wearable Network for Multi-Level Physical Fatigue Prediction in Manufacturing Workers - Dataset
<p>This dataset contains data from 43 subjects following two authentic manufacturing protocols and their self-reported fatigue score. The system employs 6 wearable sensors to continuously track vital and locomotive signs from multiple body locations.</p> <p> </p> <p>The goal of the experiment is to predict fatigue trends in a subject, while they are asked to perform pre-defined manual tasks simulating a manufacturing environment, using data from soft, flexible, wearable sensors and a vision system. The tasks in this study are repetitive and physically exerting involving intricate steps taken in real manufacturing settings. The iterative nature of the tasks facilitates comparative analyses of distinct temporal segments to characterize fatigue. The two manufacturing tasks are (1) Task Composite: Composite Sheet Layup, and (2) Task Harnessing: Wire Harnessing. The task protocol requires the subject to wear sensors to monitor vital and locomotive signs continuously. Additionally, we incorporate a weighted vest to exaggerate the induced fatigue in a reasonable duration for the study to mimic a full shift for a manufacturing worker. Each task consists of two rest periods of 5 minutes each at the start and end, as well as five segments of physical tasks. On average, each task takes a total of 1 hour. Before each data segment, the subject fills out a survey form to indicate their current fatigues as per the Borg scale. </p>
Supplemental Material to Journal Article "Probabilistic Approach for the Fatigue Strength Prediction of Polymers"
<p>This set supplements the figure and table data to the article "Probabilistic Approach for the Fatigue Strength Prediction of Polymers", DOI: <a href="https://doi.org/10.2514/1.J060444">https://doi.org/10.2514/1.J060444</a></p> <p>The Python code to reproduce the results shown in the article is released as v1.0 of the MAMO toolbox for material models DOI:</p> <p>The latest code development can be cloned at <a href="https://github.com/mrosemeier/mamo">https://github.com/mrosemeier/mamo</a>.</p> <p> </p>
Probabilistic Approach for the Fatigue Strength Prediction of Polymers
<p>The dominating factor in the fatigue of structures made from fiber reinforced polymers (FRP), for example wind turbine blades, is the polymer matrix. Traditionally, experimental stress-life data of polymers is approximated via a linear double-log Basquin model. Recently, the non-linear stress-life formulation by Stüssi was found to provide a better fit of the experimental data with a substantially reduced standard deviation. Moreover, a non-linear constant-life formulation, as proposed by Boerstra, for example, can enhance the representation of the mean stress effect compared to state-of-the-art linear models, i.e., the modified Goodman relation. To this end, we incorporated Stüssi’s model into the Boerstra relation to take account of the mean stress effects of an epoxy. This stress-life formulation was then enhanced with the Weibull probability function. The probabilistic-stress-life model provided a good approximation of the fatigue performance as a function of the stress ratio on the basis of an experimental data set. Finally, we suggested a step-wise engineering approach to derive the permissible stress-life with a view to practical design purposes. The procedure increased the reliability of the fatigue design evaluation compared to the state-of-the-art methodologies.</p> <p>Slides: <a href="https://doi.org/10.5281/zenodo.4443149">https://doi.org/10.5281/zenodo.4443149</a><br> Paper: <a href="https://doi.org/10.2514/6.2021-1289">https://doi.org/10.2514/6.2021-1289</a></p>
Plots for the publication "Lidar-assisted model predictive control of wind turbine fatigue via online rainflow-counting considering stress history"
<p>These are the raw plot files from the publication "Lidar-assisted model predictive control of wind turbine fatigue via online rainflow-counting considering stress history".</p> <p>The files have been created with MATLAB 2019, and labeled according to their corresponding figure number(s) in the publication.</p>
Nondestructive Fatigue Life Prediction for Additively Manufactured Metal Parts through a Multimodal Transfer Learning Framework
Open the record for dataset details and reuse information.
Exercise Fatigue Prediction in Healthy Individuals
ClinicalTrials.gov study NCT07066462. IPD Sharing: NO. Countries: 1. Publications: 5.
Role of Artificial Intelligence in Predicting Muscle Fatigue Using Virtual Reality Training
ClinicalTrials.gov study NCT05813613. IPD Sharing: UNDECIDED. Countries: 1. Publications: 19.
New Indices for Prediction of Human Fatigue During Training Based on Non-invasive Methods
ClinicalTrials.gov study NCT02691806. IPD Sharing: NO. Countries: 1. Publications: 0.
An Energy-Based Prognostic Framework to Predict Fatigue Damage Evolution in Composites
In this work, a prognostics framework to predict the evolution of damage in fiber-reinforced composites materials under fatigue loads is proposed. The assessment of internal damage thresholds is a challenge for fatigue prognostics in composites due to inherent uncertainties, existence of multiple damage modes, and their complex interactions. Our framework, considers predicting the balance of release strain energies from competing damage modes to establish a reference threshold for prognostics. The approach is demonstrated on data collected from a run-to-failure tension-tension fatigue experiment measuring the evolution of fatigue damage in carbon-fiber-reinforced polymer (CFRP) cross-ply laminates. Results are presented for the prediction of expected degradation by micro-cracks for a given panel with the associated uncertainty estimates.
Wearable Network for Multi-Level Physical Fatigue Prediction in Manufacturing Workers
<p>This repository contains the datasets from the 'Wearble Network for Multi-Level Physical Fatigue Prediction in Manufacturing Workers' study. The repository has been divided into 2 tasks folders Task Harnessing and Task Composite with 43 participant subfolders. </p>
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