12 skills found
boschresearch / Pylifea general library for fatigue and reliability
rstudio / Beyond Dashboard FatigueMaterials for the RStudio webinar 'Beyond Dashboard Fatigue'
RitioL / PolyFatigueCrackSimThis repository contains scripts for the batch generation of polycrystalline RVE models and finite element analysis operations. These scripts are used to simulate the generation of fatigue cracks in polycrystalline materials. The involved software includes Neper, Gmsh, and Abaqus 2022.
stupid-cooh / Metal Multiaxial Fatigue Life Prediction Using Deep LearningThis repository contains code for predicting multiaxial fatigue life of metals using deep learning models (CNN, LSTM, and GRU) combined with fully connected layers. It processes a dataset published on Materials Cloud, utilizing high-quality data to train and evaluate the models effectively.
CAEAssistant-Group / Abaqus CAE UMAT Subroutine For 3D Composite Fatigue SimulationIn this project, we modeled the fatigue behavior of a composite material in 3D space using the UMAT subroutine in Abaqus. The Abaqus .inp file and part of the UMAT subroutine are provided. To access the video tutorial and all the modeling files for this project, click the link below.
pdprop / PdpropSimulation of metal fatigue crack propagation with accounting for material memory effects
CAEAssistant-Group / Abaqus CAE UMAT Subroutine For Composite Fatigue SimulationIn this project, we simulated the fatigue behavior of a unidirectional composite material using the UMAT subroutine in Abaqus. The Abaqus .inp file, along with a portion of the UMAT subroutine, is attached. To access the video tutorial for this project and all the modeling files, visit the link below.
Santiago221 / Fatigue SN CurveAn S-N curve defines the number of cycles to failure, N ( S ) , when a material is repeatedly cycled through a given stress range S . OrcaFlex uses the S-N curve to calculate the damage in a fatigue analysis. If needed you can define a number of different S-N curves and use them at different arc lengths along a line.
boschresearch / VitemiMaterial for the paper "Micromechanical fatigue experiments for validation of microstructure-sensitive fatigue simulation models".
yakeshselvaraj / Machine Learning Model To Predict Fatigue Strength Of The MaterialIn this project we build a machine learning model to predict the fatigue strength using the Fatigue Dataset for Steel from National Institute of Material Science (NIMS) Mat Navi. Data driven approach have been used to arrive at correlation between various properties of alloys and their composition. K Fold cross validation method has been used to extract the performance evaluation metrics for each model. We were provided with different data analytics tools for predicting the fatigue strength of steels, The data has several features that are mainly categorized into Chemical Composition, Mill Product Features, and Heat Treatment parameters of various steel grades. We are provided with seven different approaches out of which we use four and come up with best and most efficient of the four approaches. Finally, we do a scatter plot for predicted vs actual fatigue strength data to see which performs better.
ilanrosc / Steel Fatigue PredictionBuild a regression model to predict steel fatigue strength based on material composition and processing conditions. This model can assist engineers in selecting optimal steel compositions for high-performance applications.
jfernandezdoroteo / Steel Bike Frame Mechanical Engineering ThesisI made a bike frame for my degree thesis. AISI 4130 cold worked steel (Columbus tubing) + fillet brazing (CuZn40). Hardness Vickers tests involved. FEA analysis (ISO tests: impact and fatigue) using Ansys Mechanical with validation of material properties and mesh sizes.
