Automated and Robust Phased Array Ultrasonic Testing (PAUT) for Weld Inspection with Seam Identification and Tracking in Large Storage Tanks

Abstract

Phased Array Ultrasonic Testing (PAUT) integrated with an autonomous robot crawler presents a promising solution for the non-destructive evaluation (NDE) of welds in large storage tanks. The crawler can be programmed to navigate along the tank surface, which automates the testing process and accelerates the scanning speed. However, most traditional PAUT crawlers lack the capability to automatically identify and track weld seams, which may result in path deviations and compromised inspection. This paper addresses this challenge by proposing a feasible and cost-effective solution that leverages a convolutional neural network (CNN) for weld seam detection, an angle differentiation algorithm for path tracking, and a controller for real-time alignment. A prototype robot car, equipped with a Raspberry Pi and a camera, is developed to validate the approach. Experimental results show that the system achieves over 80% accuracy in weld seam identification. The robot car successfully detects, tracks, and realigns itself to the weld seam even after displacement, validating the proof of concept of integrating automatic weld tracking capabilities into PAUT robot crawlers for robust NDE scanning.