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Advanced Knowledge and Modelling of Welded Materials for Ultrasonic Testing
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Advanced Knowledge and Modelling of Welded Materials for Ultrasonic Testing

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Acoustics and Vibrations".

Deadline for manuscript submissions: closed (25 May 2023) | Viewed by 13577

Special Issue Editor

Prof. Dr. Joseph Moysan

E-Mail Website
Guest Editor
Université d’Aix-Marseille, LMA UMR 7031 CNRS, 13625 Aix-en-Provence, France
Interests: nondestructive testing; welding; bonding; ultrasonics; wave propagation; structure noise, wave attenuation; nonlinear acoustics; liquid metal; data merging; automatic BSCAN thresholding

Special Issue Information

Dear Colleagues,

The purpose of this Special Issue of Applied Sciences (IF: 2.679) is to bring together advanced developments on the ultrasonic testing of welded material and state-of-the-art knowledge on welded material. All welding processes are studied including additive manufacturing (AM) such as wire-arc AM. In many cases precise ultrasonic testing is required to demonstrate the mechanical integrity of a structure. It is known that a perfect knowledge of the welded materials and the weld geometry is required to reach this goal. It is a real necessity to produce convincing modelling to help NDT experts to plan or to qualify NDT methods. The welding of dissimilar materials, anisotropic materials or heterogeneous materials and additive manufacturing produces complex and structured materials that should be described with precision to ensure a good understanding of ultrasonic testing. This Special Issue welcomes research papers aiming to ally material sciences and NDT science and to share knowledge and advances on this topic. The final goal is to assess mechanical integrity so Artificial Intelligence developments to improve UT testing of welds are also highly welcome.

We invite prospective authors to submit innovative and high-quality papers with original perspectives. The Special Issue is open to both original research articles and review articles covering all the relevant progress in these fields (including but not limited to the following):

  • Weld material description;
  • Materials vs. welding process;
  • Additive manufacturing
  • Modelling ultrasonic testing;
  • Hybrid modelling;
  • Attenuation and deviation of waves
  • Phased arrays UT
  • Inverse problems
  • Artificial Intelligence

We look forward to hearing back from you soon.

Kind regards,

Prof. Dr. Joseph Moysan
Guest Editor

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. Applied Sciences is an international peer-reviewed open access semimonthly 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 2400 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.

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Published Papers (7 papers)

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Research

19 pages, 9490 KiB  
Article
Twenty Years of Progress in Microstructure Modelling for Ultrasonic Testing, from Shielded Metal Arc Welding to Gas Tungsten Arc Welding: An Analysis for Future Developments
by Joseph Moysan, Cécile Gueudré, Marie-Aude Ploix and Gilles Corneloup
Appl. Sci. 2023, 13(19), 10852; https://doi.org/10.3390/app131910852 - 29 Sep 2023
Viewed by 1428
Abstract
To ensure and to demonstrate the mechanical integrity of a welded structure, precise ultrasonic testing (UT) is often mandatory. The importance of the link between nondestructive testing (NDT) and the assessment of structural integrity is recalled. However, it is difficult to achieve great [...] Read more.
To ensure and to demonstrate the mechanical integrity of a welded structure, precise ultrasonic testing (UT) is often mandatory. The importance of the link between nondestructive testing (NDT) and the assessment of structural integrity is recalled. However, it is difficult to achieve great efficiency as the welding of thick and heavy structural part produces heterogeneous material. Heterogeneity results from the welding process itself as well as from the material solidification laws. For thick components, several welding passes are deposited, and temperature gradients create material grain elongation and/or size variations. In many cases, the welded material is also anisotropic, this anisotropy being due to the metal used, for example, austenitic stainless steel. At the early stages of ultrasonic testing, this kind of welded material was considered too unpredictable, and thus too difficult to be tested by ultrasounds without possible diagnosis errors and misunderstandings. At the end of the 1990s, an algorithmic solution to predict the material organisation began to be developed using data included in the welding notebook. This algorithm or modelling solution was called MINA. This present work recalls, in a synthetic form, the path followed to create this algorithm combining the use of solidification laws and the knowledge of the order of passes in the case of shielded metal arc welding (SMAW). This work describes and questions the simplifications used to produce a robust algorithm able to give a digital description of the material for wave simulation code. Step by step, advances and demonstrations are described as well as the limitations, and ways to progress are sketched. Recent developments are then explained and discussed for modelling in the case of gas tungsten arc welding (GTAW), in addition to discussions about 3D modelling for the future. The discussion includes alternative ways to represent the welded material and challenges to continue to produce more and more convincing weld material model to qualify and to make use of UT methods. Full article
(This article belongs to the Special Issue Advanced Knowledge and Modelling of Welded Materials for Ultrasonic Testing)
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29 pages, 9276 KiB  
Article
Supporting Imaging of Austenitic Welds with Finite Element Welding Simulation—Which Parameters Matter?
by Michał K. Kalkowski, Zoltán Bézi, Michael J. S. Lowe, Andreas Schumm, Bernadett Spisák and Szabolcs Szavai
Appl. Sci. 2023, 13(13), 7448; https://doi.org/10.3390/app13137448 - 23 Jun 2023
Cited by 1 | Viewed by 1168
Abstract
The basic principle of ultrasound is to relate the time of flight of a received echo to the location of a reflector, assuming a known and constant velocity of sound. This assumption breaks down in austenitic welds, in which a microstructure with large [...] Read more.
The basic principle of ultrasound is to relate the time of flight of a received echo to the location of a reflector, assuming a known and constant velocity of sound. This assumption breaks down in austenitic welds, in which a microstructure with large oriented austenitic grains induces local velocity differences resulting in deviations of the ultrasonic beam. The inspection problem is further complicated by scattering at grain boundaries, which introduces structural noise and attenuation. Embedding material information into imaging algorithms usually improves image quality and aids interpretation. Imaging algorithms can take the weld structure into account if it is known. The usual way to obtain such information is by metallurgical analysis of slices of a representative mock-up fabricated using the same materials and welding procedures as in the actual component. A non-destructive alternative to predict the weld structure is based on the record of the welding procedure, using either phenomenological models or the finite element method. The latter requires detailed modelling of the welding process to capture the weld pool and the microstructure formation. Several parameters are at play, and uncertainties intrinsically affect the process owing to the limited information available. This paper reports a case study aiming to determine the most critical parameters and levels of complexity of the weld formation models from the perspective of ultrasonic imaging. By combining state-of-the-art welding simulation with time-domain finite element prediction of ultrasound in complex welds, we assess the impact of the modelling choices on the offset and spatial spreading of defect signatures. The novelty of this work is in linking welding simulation with ultrasonic imaging and quantifying the effect of the common assumptions in solidification modelling from the non-destructive examination perspective. Both aspects have not been explored in the literature to date since solidification modelling has not been used to support ultrasonic inspection extensively. The results suggest that capturing electrode tilt, welding power, and weld path correctly is less significant. Bead shape was identified as having the greatest influence on delay laws used to compute ultrasonic images. Most importantly, we show that neglecting mechanical deformation in FE, allowing for simpler thermal simulation supplemented with a phenomenological grain growth loop, does not reduce the quality of the images considerably. Our results offer a pragmatic balance between the complexity of the model and the quality of ultrasonic images and suggest a perspective on how weld formation modelling may serve inspections and guide pragmatic implementation. Full article
(This article belongs to the Special Issue Advanced Knowledge and Modelling of Welded Materials for Ultrasonic Testing)
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12 pages, 2726 KiB  
Article
Anisotropy Corrected FMC/TFM Based Phased Array Ultrasonic Imaging in an Austenitic Buttering Layer
by S. Ponseenivasan, Anish Kumar and K. V. Rajkumar
Appl. Sci. 2023, 13(8), 5195; https://doi.org/10.3390/app13085195 - 21 Apr 2023
Cited by 3 | Viewed by 1867
Abstract
For the narrow gap dissimilar weld between a ferritic steel and a nickel base superalloy, a nickel base alloy buttering layer is deposited on the ferritic steel side as an intermediate layer. The bonding between the buttering layer and the ferritic steel is [...] Read more.
For the narrow gap dissimilar weld between a ferritic steel and a nickel base superalloy, a nickel base alloy buttering layer is deposited on the ferritic steel side as an intermediate layer. The bonding between the buttering layer and the ferritic steel is required to be inspected from the buttering layer side. The buttering layer exhibits very high elastic anisotropy due to elongated columnar grains with preferred orientations. In this paper, the effect of elastic anisotropy on the phased array ultrasonic imaging of defects in the buttering layer is demonstrated for data acquired in full matrix capture (FMC) mode and reconstructed with the total focusing method (TFM). The anisotropy in the buttering layer leads to distorted flaw images, which limits the lateral resolution and defect detection sensitivity. Angle-dependent ultrasonic velocity measured in through-transmission FMC mode has been used for processing the FMC data to obtain high-resolution TFM images with improved sensitivity. The velocity values used are in line with the grain orientations observed by electron-backscatter diffraction (EBSD) studies. Further, an alternate approach is also proposed to obtain a TFM image with improved resolution using a suitable isotropic velocity. The approach can be implemented in any commercial phased array ultrasonic system having the FMC-TFM feature. Full article
(This article belongs to the Special Issue Advanced Knowledge and Modelling of Welded Materials for Ultrasonic Testing)
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15 pages, 4556 KiB  
Article
System Invariant Method for Ultrasonic Flaw Classification in Weldments Using Residual Neural Network
by Jinhyun Park, Seung-Eun Lee, Hak-Joon Kim, Sung-Jin Song and Sung-Sik Kang
Appl. Sci. 2022, 12(3), 1477; https://doi.org/10.3390/app12031477 - 29 Jan 2022
Cited by 8 | Viewed by 2163
Abstract
The industrial use of ultrasonic flaw classification using neural networks in weldments must overcome many challenges. A major constraint is the use of numerous systems, including a combination of transducers and equipment. This causes high complexity in the datasets used in the training [...] Read more.
The industrial use of ultrasonic flaw classification using neural networks in weldments must overcome many challenges. A major constraint is the use of numerous systems, including a combination of transducers and equipment. This causes high complexity in the datasets used in the training of neural networks, which decreases performance. In this study, the performance of a neural network was enhanced using signal processing on an ultrasonic weldment flaw dataset to achieve system invariance. The dataset contained 5839 ultrasonic flaw signals collected by various types of transducers connected to KrautKramer USN60. Every signal in the dataset was from 45 FlawTech/Sonaspection weldment specimens with five types of flaw: crack, lack of fusion, slag inclusion, porosity, and incomplete penetration. The neural network used in this study is a residual neural network with 19 layers. The performance evaluation of the same network structure showed that the original database can achieve 62.17% ± 4.13% accuracy, and that the invariant database using the system invariant method can achieve 91.45% ± 1.77% accuracy. The results demonstrate that using a system invariant method for ultrasonic flaw classification in weldments can improve the performance of a neural network with a highly complex dataset. Full article
(This article belongs to the Special Issue Advanced Knowledge and Modelling of Welded Materials for Ultrasonic Testing)
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17 pages, 2178 KiB  
Article
Deep Learning Based Inversion of Locally Anisotropic Weld Properties from Ultrasonic Array Data
by Jonathan Singh, Katherine Tant, Anthony Mulholland and Charles MacLeod
Appl. Sci. 2022, 12(2), 532; https://doi.org/10.3390/app12020532 - 6 Jan 2022
Cited by 6 | Viewed by 2367
Abstract
The ability to reliably detect and characterise defects embedded in austenitic steel welds depends on prior knowledge of microstructural descriptors, such as the orientations of the weld’s locally anisotropic grain structure. These orientations are usually unknown but it has been shown recently that [...] Read more.
The ability to reliably detect and characterise defects embedded in austenitic steel welds depends on prior knowledge of microstructural descriptors, such as the orientations of the weld’s locally anisotropic grain structure. These orientations are usually unknown but it has been shown recently that they can be estimated from ultrasonic scattered wave data. However, conventional algorithms used for solving this inverse problem incur a significant computational cost. In this paper, we propose a framework which uses deep neural networks (DNNs) to reconstruct crystallographic orientations in a welded material from ultrasonic travel time data, in real-time. Acquiring the large amount of training data required for DNNs experimentally is practically infeasible for this problem, therefore a model based training approach is investigated instead, where a simple and efficient analytical method for modelling ultrasonic wave travel times through given weld geometries is implemented. The proposed method is validated by testing the trained networks on data arising from sophisticated finite element simulations of wave propagation through weld microstructures. The trained deep neural network predicts grain orientations to within 3° and in near real-time (0.04 s), presenting a significant step towards realising real-time, accurate characterisation of weld microstructures from ultrasonic non-destructive measurements. The subsequent improvement in defect imaging is then demonstrated via use of the DNN predicted crystallographic orientations to correct the delay laws on which the total focusing method imaging algorithm is based. An improvement of up to 5.3 dB in the signal-to-noise ratio is achieved. Full article
(This article belongs to the Special Issue Advanced Knowledge and Modelling of Welded Materials for Ultrasonic Testing)
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15 pages, 8838 KiB  
Article
FEA-Based Ultrasonic Focusing Method in Anisotropic Media for Phased Array Systems
by Seongin Moon, To Kang, Soonwoo Han, Kyung-Mo Kim, Hyung-Ha Jin, Sung-Woo Kim, Munsung Kim and Hyunil Seo
Appl. Sci. 2021, 11(19), 8888; https://doi.org/10.3390/app11198888 - 24 Sep 2021
Cited by 2 | Viewed by 1533
Abstract
Traditional ultrasonic imaging methods have a low accuracy in the localization of defects in austenitic welds because the anisotropy and inhomogeneity of the welds cause distortion of the ultrasonic wave propagation paths in anisotropic media. The distribution of the grain orientation in the [...] Read more.
Traditional ultrasonic imaging methods have a low accuracy in the localization of defects in austenitic welds because the anisotropy and inhomogeneity of the welds cause distortion of the ultrasonic wave propagation paths in anisotropic media. The distribution of the grain orientation in the welds influences the ultrasonic wave velocity and ultrasonic wave propagation paths. To overcome this issue, a finite element analysis (FEA)-based ultrasonic imaging methodology for austenitic welds is proposed in this study. The proposed ultrasonic imaging method uses a wave propagation database to synthetically focus the inter-element signal recorded with a phased array system using a delay-and-sum strategy. The wave propagation database was constructed using FEA considering the grain orientation distribution and the anisotropic elastic constants in the welds. The grain orientation was extracted from a macrograph obtained from a dissimilar metal weld specimen, after which the elastic constants were optimized using FEA with grain orientation information. FEA was performed to calculate a full matrix of time-domain signals for all combinations of the transmitting and receiving elements in the phased array system. The proposed approach was assessed for an FEA-based simulated model embedded in a defect. The simulation results proved that the newly proposed ultrasonic imaging method can be used for defect localization in austenitic welds. Full article
(This article belongs to the Special Issue Advanced Knowledge and Modelling of Welded Materials for Ultrasonic Testing)
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14 pages, 5126 KiB  
Article
Ultrasonic Array Imaging of Nuclear Austenitic V-Shape Welds with Inhomogeneous and Unknown Anisotropic Properties
by Corentin Menard, Sébastien Robert and Dominique Lesselier
Appl. Sci. 2021, 11(14), 6505; https://doi.org/10.3390/app11146505 - 15 Jul 2021
Cited by 5 | Viewed by 1940
Abstract
The quality of ultrasound images of welds is highly penalized by the dendritic structure of the material that forms during cooling. The image of a flaw is all the more degraded as a reliable description of such a medium is most of the [...] Read more.
The quality of ultrasound images of welds is highly penalized by the dendritic structure of the material that forms during cooling. The image of a flaw is all the more degraded as a reliable description of such a medium is most of the time not possible, due to the poor knowledge on the weld at time of inspection. In a previous work, we demonstrated the efficiency of an optimization procedure to correct a degraded Total Focusing Method (TFM) image of a point-like reflector inside a homogeneous weld, with uniaxial grain orientation. In the present contribution, this procedure is extended to more realistic welds with a varying grain orientation, and it is evaluated with a 64-element array emitting at 5 MHz on stainless steel weld samples featuring side-drilled holes of 1 mm diameter. A first proof of concept with simulated echoes, and then two experiments show that defects that were hardly visible on TFM images before the optimization are now well reconstructed and with positioning errors inferior to 1 mm. Full article
(This article belongs to the Special Issue Advanced Knowledge and Modelling of Welded Materials for Ultrasonic Testing)
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