Parts Grinding Code

This repository contains all code related to the Part C Grinding Parts Count group project. The project focuses on real-time classification and visualization of grinding parts using machine learning techniques.

📁 Project Structure

parts-grinding-code/
├── CircularBuffer.py           # Implements a circular buffer for data handling
├── collect_and_train.py        # Script to collect data and train the model
├── real_time_classifier.py     # Real-time classification script
├── real_time_graphs.py         # Real-time data visualization
├── kernel_setup.txt            # Instructions for setting up file directories
├── README.md                   # Project documentation
└── LICENSE                     # MIT License

🚀 Getting Started

Prerequisites

• Python 3.8 or higher
• Recommended: Use a virtual environment to manage dependencies

Installation

1. Clone the repository:

   git clone https://github.com/Thead12/parts-grinding-code.git
   cd parts-grinding-code

2. Set up a virtual environment (optional but recommended):

   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install the required packages:

   pip install -r requirements.txt

4. Set up the file directories: Refer to kernel_setup.txt for detailed instructions on setting up the necessary file directories.Proper configuration is essential for the scripts to function correctly.

🧪 Usage

Collecting Data and Training the Model

Use collect_and_train.py to collect data and train your machine learning model.

python collect_and_train.py

Ensure that your data is organized as specified in kernel_setup.txt before running this script.

Real-Time Classification

Run real_time_classifier.py to perform real-time classification of grinding parts.

python real_time_classifier.py

Real-Time Visualization

Use real_time_graphs.py to visualize data in real-time.

python real_time_graphs.py

🛠️ Configuration

The project utilizes CircularBuffer.py to manage data streams efficiently. Ensure that all configurations are set as per the guidelines in kernel_setup.txt. networktest.py can be updated from IP addresses to work with MQTT frameworks to automatically run the other scripts when the grinding machine changes state.

📄 License

This project is licensed under the MIT License. See the LICENSE file for details.

🤝 Contributing

Contributions are welcome! Please fork the repository and submit a pull request for any enhancements or bug fixes.

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For any issues or questions, please open an issue in the repository.

Python library for managing long running tasks, we can use this to initialise and close the program automatically I believe. https://supervisord.org/