STREAM AI - Tip Tracker & Anomaly Detection

Overview

This project aims to provide a real-time solution to common 3D printing errors, such as over-extrusion, under-extrusion, and inconsistencies caused by bubbles in the extruder. Our system's end goal is to use real-time video, gcode and data from the digital twin sensors to monitor the printing process, track the extruder tip's position, identify printing errors and make real-time adjustments to the print based on the measured width of the extruded material.

There are many sources of error in tracking the tip. To counter these we created methods to detect, process and correct tips. Further details can be found:

• Short Written Report
• Video Presentation

Pre-requisites

• Python >= 3.x
• pip

Installation Steps

Clone the Repository

git clone https://github.com/yourusername/yourproject.git](https://github.com/BrianP8701/STREAM.AI.git
cd [Path to this project]

Create a Virtual Environment

For macOS and Linux:

python3 -m venv myenv

For Windows:

python -m venv myenv

Activate the Virtual Environment

For macOS and Linux:

source myenv/bin/activate

For Windows:

.\myenv\Scripts\activate

Install Dependencies

pip install -r requirements.txt

Usage

To run the system, simply choose your video, gcode and signals path on main.py and run.

Contributing

Bug Reporting: Should you stumble upon any bugs or challenges, we appreciate detailed issue reports. Please create a new issue, outlining the encountered problem and the specific inputs you used.

Optimizations & Refinements: If you discover ways to enhance the system's efficiency, improve robustness, or streamline the code, kindly submit a pull request with your proposed changes.

Thank you!