Modern Non-destructive Testing for Metallic Materials

A special issue of Metals (ISSN 2075-4701). This special issue belongs to the section "Metal Failure Analysis".

Deadline for manuscript submissions: 31 October 2024 | Viewed by 5497

Special Issue Editors


E-Mail Website1 Website2
Guest Editor
Fraunhofer Institute for Nondestructive Testing IZFP, 66123 Saarbrücken, Germany
Interests: NDE4.0; AI empowered NDE; cognitive sensor systems; nondestructive material characterization and evaluation

E-Mail Website
Guest Editor
Fraunhofer Institute for Integrated Circuits (IIS), Department X-Ray Technology Center, Flugplatzstraße 75, 90768 Fürth, Germany
Interests: X-ray imaging; computed tomography; signal- and image processing; artificial intelligence; 3-D image reconstruction; X-ray physics; NDT 4.0

E-Mail Website1 Website2
Guest Editor
Fraunhofer Institute for Nondestructive Testing IZFP, Campus E3 1, 66123 Saarbrücken, Germany
Interests: NDE; NDE4.0; data processing and analysis methods

Special Issue Information

Dear Colleagues,

We are writing to you to invite you to participate in a Special Issue of Metals entitled Modern Non-Destructive Testing for Metallic Materials.

As editors, we are interested in the most recent developments and discoveries in the field of non-destructive testing methods. These may include new microscopic techniques, the latest improvements in X-ray and ultrasonic 3D-imaging, as well as acoustic, electromagnetic, and thermal inspection methods.

All contributions on the latest testing or material characterization methods should focus on metallic materials, at least as their main application. In addition, we intend to cover the full range of spatial resolutions from microns down to the nanometer scale.  Our objectives are the detection of defects and imperfections, as well as explanations of structure–property relationships, in order to characterize materials’ behavior.

We appreciate your particular expertise in one or more of the fields mentioned above. Therefore, we would like you to consider contributing to this Special Issue. Your manuscript will be very welcome and proofread by distinguished experts in the field of non-destructive testing.

Prof. Dr. Bernd Valeske
Dr. Theobald Fuchs
Dr. Ralf Tschuncky
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Metals is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • non-destructive inspection and evaluation of materials
  • Eddy current
  • electromagnetic
  • X-ray
  • micromagnetic
  • ultrasound and acoustics
  • microwave
  • terahertz
  • tomography
  • microscopy
  • 2D/3D-imaging
  • AI based evaluation
  • NDE4.0 applications
  • thermography

Published Papers (2 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

20 pages, 4354 KiB  
Article
Industrial X-ray Image Analysis with Deep Neural Networks Robust to Unexpected Input Data
by Erik Lindgren and Christopher Zach
Metals 2022, 12(11), 1963; https://doi.org/10.3390/met12111963 - 16 Nov 2022
Cited by 3 | Viewed by 1986
Abstract
X-ray inspection is often an essential part of quality control within quality critical manufacturing industries. Within such industries, X-ray image interpretation is resource intensive and typically conducted by humans. An increased level of automatization would be preferable, and recent advances in artificial intelligence [...] Read more.
X-ray inspection is often an essential part of quality control within quality critical manufacturing industries. Within such industries, X-ray image interpretation is resource intensive and typically conducted by humans. An increased level of automatization would be preferable, and recent advances in artificial intelligence (e.g., deep learning) have been proposed as solutions. However, typically, such solutions are overconfident when subjected to new data far from the training data, so-called out-of-distribution (OOD) data; we claim that safe automatic interpretation of industrial X-ray images, as part of quality control of critical products, requires a robust confidence estimation with respect to OOD data. We explored if such a confidence estimation, an OOD detector, can be achieved by explicit modeling of the training data distribution, and the accepted images. For this, we derived an autoencoder model trained unsupervised on a public dataset with X-ray images of metal fusion welds and synthetic data. We explicitly demonstrate the dangers with a conventional supervised learning-based approach and compare it to the OOD detector. We achieve true positive rates of around 90% at false positive rates of around 0.1% on samples similar to the training data and correctly detect some example OOD data. Full article
(This article belongs to the Special Issue Modern Non-destructive Testing for Metallic Materials)
Show Figures

Figure 1

20 pages, 9463 KiB  
Article
Selection of Higher Order Lamb Wave Mode for Assessment of Pipeline Corrosion
by Donatas Cirtautas, Vykintas Samaitis, Liudas Mažeika, Renaldas Raišutis and Egidijus Žukauskas
Metals 2022, 12(3), 503; https://doi.org/10.3390/met12030503 - 16 Mar 2022
Cited by 13 | Viewed by 2372
Abstract
Hidden corrosion defects can lead to dangerous accidents such as leakage of toxic materials causing extreme environmental and economic consequences. Ultrasonic guided waves showed good potential detecting distributed corrosion in pipeline networks at sufficiently large distances. To simplify signal analysis, traditional guided wave [...] Read more.
Hidden corrosion defects can lead to dangerous accidents such as leakage of toxic materials causing extreme environmental and economic consequences. Ultrasonic guided waves showed good potential detecting distributed corrosion in pipeline networks at sufficiently large distances. To simplify signal analysis, traditional guided wave methods use low frequencies where only fundamental modes exist; hence, the small, localized defects are usually barely detectable. Novel techniques frequently use higher order guided wave modes that propagate around the circumference of the pipe and are more sensitive to the localized changes in the wall thickness. However current high order mode guided wave technology commonly uses either non-dispersive shear modes or higher order mode cluster (HOMC) waves that are mostly sensitive to surface defects. As the number of application cases of high order modes to corrosion detection is still limited, a huge potential is available to seek for other modes that can offer improved resolution and sensitivity to localized corrosion type defects. The objective of this work was to investigate higher order modes for corrosion detection and to determine the most promising ones in sense of excitability, leakage losses, propagation distance, and potential simplicity in the analysis. The selection of the proper mode is discussed with the support of phase and group velocity dispersion curves, out of plane and in plane distributions over the thickness and on surface of the sample, and leakage losses due to water load. The analysis led to selection of symmetric S3 mode, while the excitation of it was demonstrated through finite element simulations, taking into account the size of phased array aperture and apodization law and considering two-sided mode generation. Finally, theoretical estimations were confirmed with experiments, demonstrating the ability to generate and receive selected mode. It was shown that S3 mode is a good candidate for corrosion screening around the circumference of the pipe, as it has sufficient propagation distance, can be generated with conventional ultrasonic (UT) phased arrays, has sufficiently high group velocity to be distinguished from co-existing modes, and is sensitive to the loss of wall thickness. Full article
(This article belongs to the Special Issue Modern Non-destructive Testing for Metallic Materials)
Show Figures

Figure 1

Back to TopTop