Automated Robotic and AI-Driven Nondestructive Inspection for Enhanced Welding Flaw Detection

Welding is indispensable in structural steel members due to its ability to create strong, durable, and versatile connections essential for constructing safe, efficient, and resilient infrastructure. Safety and adequate functioning of these important infrastructures depend on accurate design, construction, and finally, the inspection and quality investigation. Inspection is important to ensure there is no substantial flaw and defect in the infrastructure members due to the construction or operational process. Quality assurance measures such as nondestructive testing (NDT) can be employed to verify the integrity of welds, providing confidence in the structural integrity of steel members. Among the variety of NDT methods, configurations of ultrasonic testing (UT) are identified as efficient techniques of inspection for welding. Despite the effectiveness of the traditional UT techniques, the accuracy and repeatability of the tests can be significantly improved via integrating intelligent robotics into advanced phased array UT (PAUT) method. This study aims to advance NDT of welded structures by incorporating robotic inspection systems for enhanced flaw detection. Traditional welding inspection methods are often labor-intensive, subjective, and limited by human expertise and accessibility in complex or hazardous environments. This research seeks to overcome these limitations by developing an automated inspection framework that produces robotic scanning precision detection and characterization of welding flaws.