Sizing-Based Flaw Acceptability in Weldments Using Phased Array Ultrasonic Testing and Neural Networks

Abstract

Liquefied Natural Gas (LNG) is one of the major renewable energy sources and is stored and carried in a storage tank that is designed following international standards. Since LNG becomes highly unstable when it encounters oxygen in the air, a leakage from an LNG storage tank can cause a catastrophic industrial accident. Thus, the inspection of LNG storage tanks is one of the priorities to be completed before LNG is stored in a storage tank. Recently, the usage of Phased Array Ultrasonic Testing (PAUT) has been gradually increasing as the risks of RT emerge. PAUT has some obstacles to overcome in order to substitute RT, such as efficiency and accuracy. Specifically, the cost issue must be addressed. Therefore, many attempts to combine PAUT with Artificial Neural Networks (ANN) have been made. PAUT provides many types of 2D images of the inspected weldment. The S-scan is one of the 2D images provided by PAUT, and it displays the cross-sectional view of the specimen with a single transducer. The inspectors examine the S-scan image and other provided images of PAUT to detect, classify and size the flaw that exists in the weldment so that the decision of whether the inspected weldment with the flaw is acceptable can be made. Nowadays, most of the previous research on PAUT and ANN focuses on detecting and classifying the flaws in B-scan or S-scan images. However, the last step to determine the flaws’ acceptability is not yet covered. In this study, the flaw acceptance criteria of PAUT in various international standards are listed. EXTENDE CIVA is used to create the PAUT S-scan images. The S-scan images are labeled with the listed acceptance criteria. Then, they are used in Mask R-CNN training. After the training, some new S-scan images with flaws are used to test the performance, and this showed 96% precision and 87% recall. With the algorithm, the acceptability of a flaw in a weldment can be determined efficiently and it will reduce the burden of PAUT usage and reduce the time required for a full-length inspection.

Keywords

• Phased Array Ultrasonic Testing
• Mask R-CNN
• flaw acceptance criteria
• Artificial Neural Network