Recent Advances in Ultrasonic Nondestructive Evaluation

A special issue of NDT (ISSN 2813-477X).

Deadline for manuscript submissions: closed (30 June 2024) | Viewed by 2728

Special Issue Editor


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Guest Editor
1. Centre for Advanced and Smart Technologies, University of Northampton, Northampton NN1 5PH, UK
2. Centre for Sustainable Futures, University of Northampton, Northampton NN1 5PH, UK
Interests: photonics devices and systems; non-destructive testing and condition monitoring

Special Issue Information

Dear Colleagues,

Ultrasonic testing can be used to interrogate targets nondestructively for the purpose of object characterization, defect detection, structural health monitoring, process monitoring, remote sensing, and imaging. In recent years, there have been new developments in this field in various aspects such as sensor design, instrument development, new algorithms, and applications. This Special Issue of NDT on “Recent Advances in Ultrasonic Nondestructive Evaluation” welcomes contributions from the following topics:

  • Phased array ultrasonic testing;
  • Long range ultrasonic testing;
  • Defect detection and reliability of advanced ultrasonic NDT;
  • Ultrasonic sensor design and optimisation;
  • Novel ultrasonic NDT;
  • Advanced signal and image processing;
  • Material characterisation.

Dr. Abdeldjalil Bennecer
Guest Editor

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Keywords

  • nondestructive testing
  • phased array ultrasonic testing
  • time-of-flight-diffraction
  • guided wave ultrasonic testing
  • IRIS ultrasonic testing
  • electromagnetic acoustic transducers

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Published Papers (1 paper)

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Research

20 pages, 21130 KiB  
Article
Automated Weld Defect Detection in Industrial Ultrasonic B-Scan Images Using Deep Learning
by Amir-M. Naddaf-Sh, Vinay S. Baburao and Hassan Zargarzadeh
NDT 2024, 2(2), 108-127; https://doi.org/10.3390/ndt2020007 - 7 Jun 2024
Viewed by 1823
Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Recent Advances in Ultrasonic Nondestructive Evaluation)
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