EDM

Deep learning system for studying and classifying EDM (Electrical Discharge Machining) drilling processes using temporal segmentation.

Components

• Data Analysis: Statistical analysis and visualization tools
• Stage Segmentation: Temporal Convolutional Network (TCN) for stage segmentation

Future: Transfer learning models for additional EDM classification tasks

🚀 Quick Start

Prerequisites

IMPORTANT: Before running any code, you need to add the Data folder with the following structure:

Data/
└── Option 2/
    ├── Train/
    │   ├── Normal/
    │   ├── NPT/
    │   ├── OD/
    │   └── ... (each containing CSV files)
    └── Test/
        ├── Normal/
        ├── NPT/
        ├── OD/
        └── ...

Each CSV file should contain: Voltage, Z, and Segment columns.

Installation

1. Clone the repository:

git clone https://github.com/francomartino2003/EDM.git
cd EDM

2. Create and activate conda environment:

conda create -n edm python=3.10
conda activate edm

3. Install dependencies:

pip install -r requirements.txt

4. (Optional) For CUDA support:

pip install torch torchvision --index-url https://download.pytorch.org/whl/cu126

Optional: Generate Data Visualizations

Before running experiments, you can optionally generate visualization plots:

cd "Data Analysis"
python create_visualizations.py

This creates the Data Viz folder with plots for all time series. Note: This takes time.

Running Stage Segmentation

cd "Stage Segmentation"
python run_experiment.py

Configure hyperparameters in config.py before running. See Stage Segmentation/README.md for details.

📁 Project Structure

EDM/
├── Data/                          # (Excluded from repo) Raw CSV data
├── Data Analysis/
│   ├── README.md                  # Data analysis documentation
│   ├── create_visualizations.py   # Visualization generation
│   ├── data_analysis.py           # Statistical analysis
│   └── Data Viz/                  # (Excluded) Generated visualizations
├── Stage Segmentation/
│   ├── config.py                  # Experiment configuration
│   ├── preprocessing.py           # Data preprocessing
│   ├── model.py                   # TCN architecture
│   ├── train.py                   # Training script
│   ├── evaluate.py                # Evaluation script
│   ├── visualize_predictions.py   # Prediction visualization
│   ├── run_experiment.py          # Main execution script
│   ├── results/                   # Experiment results
│   └── README.md                  # Detailed documentation
├── requirements.txt               # Python dependencies
└── README.md                      # This file

🔧 Dependencies

See requirements.txt for complete list. Main dependencies:

• Python 3.10+
• PyTorch 2.0+
• NumPy, Pandas, scikit-learn
• Matplotlib, Seaborn

Author

Franco Martino - francomartino2003@gmail.com

Repository: https://github.com/francomartino2003/EDM