Optimising laser-induced phased-arrays for defect detection in continuous inspections

Cantero-Chinchilla, Sergio; Croxford, Anthony J.; Wilcox, Paul D.

doi:10.1016/j.ndteint.2024.103091

Cited by 1 publication

(1 citation statement)

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“…The advantage of employing NDT methods is that the structures can be inspected without interruptions in their daily functions, offering a nonintrusive means of assessing structural health. NDT is widely used in safety-critical industries such as aerospace, automotive, and oil and gas, where the reliability of structures plays an important role.To enhance the overall confidence and efficiency of the inspection process, researchers are increasingly exploring new techniques such as optimizing laser-induced arrays for defect detection [1], implementing automated computer-aided inspection for real-time defect detection in laser-welded blanks [2], etc. In comparison to other NDT methods, UT boasts a long history of effectively detecting subsurface [3] and small flaws [4].…”

Section: Introductionmentioning

confidence: 99%

Automated Weld Defect Detection in Industrial Ultrasonic B-Scan Images Using Deep Learning

Naddaf-Sh,

Baburao,

Zargarzadeh

2024

NDT

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Automated ultrasonic testing (AUT) is a nondestructive testing (NDT) method widely employed in industries that hold substantial economic importance. To ensure accurate inspections of exclusive AUT data, expert operators invest considerable effort and time. While artificial intelligence (AI)-assisted tools, utilizing deep learning models trained on extensive in-laboratory B-scan images, whether they are augmented or synthetically generated, have demonstrated promising performance for automated ultrasonic interpretation, ongoing efforts are needed to enhance their accuracy and applicability. This is possible through the evaluation of their performance with experimental ultrasonic data. In this study, we introduced a real-world ultrasonic B-scan image dataset generated from proprietary recorded AUT data during industrial automated girth weld inspection in oil and gas pipelines. The goal of inspection in our dataset was detecting a common type of defect called lack of fusion (LOF). We experimentally evaluated deep learning models for automatic weld defect detection using this dataset. Our assessment covers the baseline performance of state-of-the-art (SOTA) models, including transformer-based models (DETR and Deformable DETR) and YOLOv8. Their flaw detection performance in ultrasonic B-scan images has not been reported before. The results show that, without heavy augmentations or architecture customization, YOLOv8 outperforms the other models with an F1 score of 0.814 on our test set.

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