Fatigue strength assessment of advanced metallic alloys
Overview:
We present a conditional probability framework to address the challenge of fatigue assessment in metallic alloys with limited data. This approach enhances prediction reliability and supports advanced alloy design by effectively addressing material variability and uncertainty.
This repository provides the implementation and insights derived from a conditional probability framework designed to assess fatigue in new and unexplored metallic alloys, with a focus on Multi-Principal Element Alloys (MPEAs). The method leverages probabilistic approaches to address material variability and model uncertainties, paving the way for advanced alloy design and fatigue prediction.
Fatigue prediction in advanced metallic materials, such as MPEAs, is often hindered by limited experimental data. This project introduces a novel framework to overcome these challenges by:
Employing conditional probability to model uncertainties in fatigue behaviour.
Incorporating probabilistic tools to address both inherent material variability and model-based uncertainties.
Utilising experimental fatigue data for alloys, including CoCrFeMnNi and AlCoCrFeMnNi, under different stress ratios (R = 0.1 and R = −1).
The framework provides a robust basis for predicting fatigue trends, supporting reliable decision-making in material design.
Key Features:
Probabilistic Fatigue Assessment: Handles uncertainty in fatigue data, moving beyond conventional deterministic approaches.
Material-Specific Insights: Captures distinct fatigue behaviors for FCC microstructures in MPEAs.
Support for Advanced Alloy Design: Enables informed decisions and improved predictions for future material development.
Repository Structure:
/data/: Contains fatigue data for CoCrFeMnNi and AlCoCrFeMnNi alloys.
/models/: Probabilistic models and scripts for fatigue assessment.
/results/: Key findings and trends identified through the framework.
Usage:
Preprocess experimental data using scripts in /data.
Apply probabilistic models from /models to analyse fatigue behaviour.
Explore insights and decision-making tools in /results.
Contributions:
Contributions are welcome! If you have suggestions or improvements, feel free to contact me.
Citation
If you use this framework in your research, please cite the related article:
@article{YILDIRIM2024117358,
title = {Data-driven conditional probability to predict fatigue properties of multi-principal element alloys (MPEAs)},
journal = {Computer Methods in Applied Mechanics and Engineering},
volume = {432},
pages = {117358},
year = {2024},
issn = {0045-7825},
doi = {https://doi.org/10.1016/j.cma.2024.117358},
url = {https://www.sciencedirect.com/science/article/pii/S0045782524006133},
author = {Halid Can Yıldırım and Peter K. Liaw},
keywords = {Multi-principal element alloys, Fatigue, Conditional Probability, Uncertainty},
}
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