pyfatiguepro 0.1.0

Physics-informed fatigue-life prediction library with FEA ingestion, proprietary data validation, ML, SHAP, FastAPI, Docker, and CI/CD.

PyFatiguePro

Physics-informed fatigue-life prediction library for engineering and aeroengine alloys.

PyFatiguePro combines classical fatigue mechanics, FEA result ingestion, proprietary experimental data validation, machine learning, SHAP explainability, uncertainty estimation, REST API deployment, Docker, tests, and CI/CD.

Features

• Basquin S-N high-cycle fatigue model
• Coffin-Manson strain-life low-cycle fatigue model
• Paris Law crack-growth integration
• Goodman and Gerber mean-stress corrections
• Von Mises multiaxial stress calculation
• FEA CSV ingestion from Abaqus/ANSYS-style exports
• FEA hotspot feature extraction
• Proprietary-data validation and anonymization
• Dataset provenance registry
• Scalable ML training using Random Forest / HistGradientBoosting / GPR
• Batch prediction for large CSV files
• Uncertainty estimation
• SHAP explainability support
• Sensitivity analysis
• FastAPI REST API
• Docker deployment
• GitHub Actions CI/CD

Data disclaimer

This package does not ship proprietary or unpublished fatigue datasets. It provides safe workflows to validate, anonymize, and train models on your own internal/proprietary fatigue-test data.

Synthetic examples are demonstration-only and must not be advertised as physical-test validation.

Installation after PyPI upload

pip install pyfatiguepro

Local development install

pip install -e ".[dev,explain]"

Quick example

from pyfatiguepro.core import fit_basquin, basquin_life

stress = [760, 700, 650, 600, 550]
cycles = [1e4, 3e4, 8e4, 2e5, 7e5]

fit = fit_basquin(stress, cycles)
print(fit)

predicted = basquin_life(625, fit["sigma_f_prime"], fit["b"])
print(predicted)

Train demo model

python examples/train_demo.py

This creates model.joblib.

Run API

uvicorn pyfatiguepro.api:app --host 0.0.0.0 --port 8002

Open:

http://127.0.0.1:8002/docs

Docker

docker build -t pyfatiguepro .
docker run -p 8002:8002 pyfatiguepro

PyPI upload

python -m pip install --upgrade build twine
python -m build
twine check dist/*
twine upload --repository testpypi dist/*
twine upload dist/*

After successful real PyPI upload:

pip install pyfatiguepro