Building an AI-Powered Robotic Welding Defect Detection System

An automated welding defect detection system was developed using Roboflow's RF-DETR model and Gemini 2.5 Pro to enhance weld quality inspection processes, crucial in manufacturing workflows reliant on robotic welding. Despite controlled welding parameters, defects can arise, necessitating early detection to prevent faulty components from advancing in production. The global welding market is experiencing significant growth, emphasizing the importance of automated inspection systems that utilize computer vision for evaluating weld quality consistently and with minimal human intervention. The RF-DETR model is trained to identify and classify welds as "Good Weld" or "Bad Weld" using a dataset with varied visual representations, enabling the model to generalize across different inspection scenarios. Once trained, the model's performance is evaluated through metrics like precision and recall, and then deployed in Roboflow Workflows alongside Gemini 2.5 Pro, which generates inspection summaries based on the model's predictions. This integration results in an efficient inspection process where weld regions are visually marked, and quality observations are summarized, facilitating easier review by operators and quality engineers. The tutorial underscores the potential of computer vision to automate quality inspection and improve early detection of welding issues, paving the way for more reliable manufacturing processes.