About our project

In the fast-evolving landscape of manufacturing technology, the automation of welding systems demands enhanced precision, efficiency, and reliability. This Capstone project endeavors to address the pressing need for effective welding monitoring in industrial settings by proposing an innovative solution that harnesses the capabilities of the SICK Ranger Cameras combined with a laser profiler.

Our primary goal is to develop a comprehensive tool capable of performing fundamental weld bead measurements, identifying welding errors, and quantifying them. This system integrates ROS2 (Robot Operating System 2) as a framework to control the sensors and offer a holistic view of the welding process.

Results

The project has successfully delivered a prototype system that demonstrates the feasibility and effectiveness of welding monitoring using a Ranger Camera and laser technology. The key achievements include:

Detailed 3D imaging of weld beads with the fully functional SICK Ranger E55 Camera setup.

A program that accurately identifies and measures Excessive and Sagging welding defects, storing this data for quality control and analysis.

An architecture that allows for seamless integration of new sensors in the future.

This project sets a foundation for transformative changes in welding system analysis and opens avenues for research and development in manufacturing technology.

Future development

While the current prototype represents a significant step towards a comprehensive system capable of performing real-time weld bead measurements, the project harbors the potential for further development, which includes:

• Machine learning algorithms: One can incorporate machine learning to improve the accuracy of error detection and even classification, enabling the system to adapt to different welding processes and materials.

• Expanded sensor integration: Add more sensor types to the system for even more detailed analysis of the welding environment, such as thermal cameras, pyrometers, etc.