Aeroengine Fatigue Life

Machine learning for predicting fatigue life of aeroengine alloys

?? Overview

Aeroengine Fatigue Life is a production-grade machine learning library for predicting fatigue life of aeroengine alloys.

? Features

• ?? Real literature data from 200+ research articles
• ?? Multiple ML models: Random Forest, XGBoost, Neural Network
• ?? Feature analysis with variance-based selection
• ?? FastAPI server for production deployment
• ?? One-line install: pip install aeroengine-fatigue-life

?? Installation

pip install aeroengine-fatigue-life 

?? Quick Start

from aeroengine_fatigue_life import create_predictor 
import pandas as pd 
 
# Create predictor 
predictor = create_predictor(model_type="rf") 
 
# Prepare input 
sample = pd.DataFrame({ 
    "temp_c": [20], 
    "stress_mpa": [900], 
    "strain_range": [0.008], 
    "ut": [1275], 
    "ys": [1035], 
    "el": [12], 
    "hardness_hv": [480], 
    "frequency_hz": [0.5], 
}) 
 
# Make prediction 
prediction = predictor.predict(sample) 
print(f"Predicted cycles: {int(prediction[0]):,}") 

?? API Server

aeroengine-fatigue-life-server 

?? Included Alloys

• GH4169 (Ni-based superalloy)
• Inconel 718
• GH4586 (rocket engine turbine)
• Ti-6Al-4V (compressor blades)
• RR1000 (advanced Ni superalloy)
• Waspaloy (turbine disks)

?? License

MIT License - free for academic and commercial use

????? Author

Gowreeswari

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