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Thermal framework for microstructure prediction in Direct Energy Deposition - Additive Manufacturing (DED-AM) process

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MIFASOL - Thermal framework for additive manufacturing analysis

MIFASOL is a research project dedicated to the thermal analysis and modeling of parts during the Directed Energy Deposition - Additive Manufacturing (DED-AM) process. It aims to provide a modular and extensible Python framework to simulate, monitor, and analyze thermal behavior during fabrication.

The core package, TherMIFASOL, contains code developed during Quentin Dollé’s Ph.D. thesis, and includes tools for:

  • Real-time sensor monitoring
  • Experimental data processing
  • Thermal modeling
  • Utilities and numerical tools

The project also includes:

  • Experimental data acquired during multiple test campaigns
  • Calibration datasets
  • Technical documentation of the measurement devices

Note: The experimental data (data/) is large and stored externally (e.g., local server or external disk), but may be shared upon request.


Project Structure

MIFASOL/
├── TherMIFASOL/
│   ├── __init__.py
│   ├── core/
│   │   ├── sensor_monitoring/
│   │   ├── data_management/
│   │   ├── models/
│   │   └── utils/
│   ├── resources/            # Literature-based data and calibration tables
│   └── notebooks/            # Jupyter notebooks for analysis and demos
├── data/                     # External, not tracked by Git
│   ├── raw data from experiences/
│   └── data for calibration/
├── documentation/           # Technical specs of devices
│   ├── device_1/
│   ├── device_2/
│   └── ...
├── requirements.txt
├── setup.py
└── README.md

Quick Start

1. Install dependencies

Create a virtual environment and install dependencies:

python -m venv .venv
source .venv/bin/activate   # On Windows: .venv\Scripts\activate
pip install -r requirements.txt

2. Install the package

pip install -e .

3. Uninstall

pip uninstall TherMIFASOL

Dependencies

Library Purpose
numpy Numerical computations
matplotlib Visualization
scipy Scientific functions
pandas Data handling
... ...

License / Citation

Distributed for academic and research purposes. Please cite the author or the corresponding publication if used. DOI


Author

Developed by Quentin Dollé during his Ph.D. thesis.
mel: quentin.dolle@polytechnique.edu
Feel free to open issues or contribute.

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Thermal framework for microstructure prediction in Direct Energy Deposition - Additive Manufacturing (DED-AM) process

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