Metadata-Version: 2.4
Name: aeroengine-fatigue-life
Version: 1.0.0
Summary: ML-based fatigue life prediction for aeroengine alloys
Home-page: https://github.com/RAVINDRANADHBOBBILI/aeroengine-fatigue-life
Author: RAVINDRANADH BOBBILI
Author-email: ravindranadhb@gmail.com
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Python: >=3.8
Description-Content-Type: text/markdown
Requires-Dist: numpy>=1.21.0
Requires-Dist: pandas>=1.3.0
Requires-Dist: scikit-learn>=1.0.0
Requires-Dist: xgboost>=1.7.0
Requires-Dist: torch>=1.10.0
Requires-Dist: matplotlib>=3.4.0
Provides-Extra: server
Requires-Dist: fastapi>=0.100.0; extra == "server"
Requires-Dist: uvicorn>=0.23.0; extra == "server"
Requires-Dist: python-multipart>=0.0.6; extra == "server"
Dynamic: author
Dynamic: author-email
Dynamic: classifier
Dynamic: description
Dynamic: description-content-type
Dynamic: home-page
Dynamic: provides-extra
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# Aeroengine Fatigue Life 
 
**Machine learning for predicting fatigue life of aeroengine alloys** 
 
[![PyPI version](https://badge.fury.io/py/aeroengine-fatigue-life.svg)](https://pypi.org/project/aeroengine-fatigue-life/) 
[![Python 3.8+](https://img.shields.io/badge/python-3.8+-blue.svg)](https://www.python.org/downloads/) 
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT) 
 
## ?? 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 
 
```bash 
pip install aeroengine-fatigue-life 
``` 
 
## ?? Quick Start 
 
```python 
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 
 
```bash 
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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