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Applied Artificial Intelligence for Industrial Nondestructive Evaluation NDE4.0
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Applied Artificial Intelligence for Industrial Nondestructive Evaluation NDE4.0

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Industrial Technologies".

Deadline for manuscript submissions: 20 March 2025 | Viewed by 6350

Special Issue Editors

Prof. Dr. Ahmad Osman

E-Mail Website
Guest Editor
1. Fraunhofer Institute for Nondestructive Testing IZFP, 66123 Saarbruecken, Germany
2. School of Engineering, University of Applied Sciences, 66123 Saarbruecken, Germany
Interests: applied artificial intelligence; NDE 4.0; deep learning; data interpretation and analysis; explainable AI
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Valérie Kaftandjian-Doudet

E-Mail Website
Guest Editor
INSA-Lyon, LVA, EA 677, CEDEX, 69621 Villeurbanne, France
Interests: image processing; data fusion; defect classification; applied artificial intelligence; NDE 4.0; deep learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Deep learning (DL) is currently the most important discipline of Artificial Intelligence (AI). It enables machines to process data, learn patterns, and recognize and classify complex defects. By means of DL, problems whose mathematical descriptions are difficult or impossible can be processed automatically. In close combination with NDE sensors, process data, and robotics, the application of DL opens up new possibilities that will define the next generation of NDE systems. The NDE community is trending toward the NDE 4.0, and AI, especially DL, is a driver of the paradigm shift in this direction.

This Special Issue will publish high-quality, original research papers, in the overlapping fields of:

  • Deep learning for NDE image reconstruction;
  • Deep learning for data processing (signal or image);
  • NDE 4.0 systems with AI as assisting technology;
  • Deep learning for NDE data interpretation, including defect recognition;
  • Interaction between NDE–personal and AI;
  • NDE big data applications, algorithms, and systems;
  • Cloud/edge/fog computing for NDE applications;
  • Trusted AI for NDE applications;
  • Qualification of AI methods for NDE applications: norms, benchmarks, metrics, etc.

Because of the significant relevance of this Special Issue on AI in NDE, the first 10 accepted papers will be published with full waivers. Up to 15 papers are expected to be accepted for this issue.

Prof. Dr. Ahmad Osman
Prof. Dr. Valérie Kaftandjian-Doudet
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. 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.

Keywords

  • artificial intelligence
  • deep learning
  • nondestructive evaluation
  • defect recognition
  • NDE 4.0
  • NDE data

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Published Papers (3 papers)

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19 pages, 11591 KiB  
Article
Industrial Application of AI-Based Assistive Magnetic Particle Inspection
by Julien Baumeyer, Hermine Chatoux, Arnaud Pelletier and Patrick Marquié
Appl. Sci. 2024, 14(4), 1499; https://doi.org/10.3390/app14041499 - 12 Feb 2024
Cited by 1 | Viewed by 1340
Abstract
Magnetic Particle Inspection (MPI) is one of the most used methods in Non-Destructive Testing (NDT), allowing precise and robust defect detection on industrial-grade manufactured parts. However, human controllers perform this task in full black environments under UV-A lighting only (with safety glasses) and [...] Read more.
Magnetic Particle Inspection (MPI) is one of the most used methods in Non-Destructive Testing (NDT), allowing precise and robust defect detection on industrial-grade manufactured parts. However, human controllers perform this task in full black environments under UV-A lighting only (with safety glasses) and use chemical products in a confined environment. Those constraints tends to lower control performance and increase stress and fatigue. As a solution, we propose an AI-based assistive machine (called “PARADES”) inside the hazardous environment, remotely manipulated by a human operator, outside of the confined area, in cleaner and safer conditions. This paper focuses on the development of a complete industrial-grade AI machine, both in terms of hardware and software. The result is a standalone assistive AI-based vision system, plug-and-play and controller-friendly, which only needs the usual power supply 230 V plug that detects defects and measures defect length. In conclusion, the PARADES machines address for the first time the problem of occupational health in MPI with an industrial standalone machine which can work on several parts and be integrated into current production lines. Providing cleaner and healthier working conditions for operators will invariably lead to increased quality of detection. These results suggest that it would be beneficial to spread this kind of AI-based assistive technology in NDT, in particular MPI, but also in Fluorescent Penetrant Testing (FPT) or in visual inspection. Full article
(This article belongs to the Special Issue Applied Artificial Intelligence for Industrial Nondestructive Evaluation NDE4.0)
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13 pages, 5710 KiB  
Article
A Dataset of Pulsed Thermography for Automated Defect Depth Estimation
by Ziang Wei, Ahmad Osman, Bernd Valeske and Xavier Maldague
Appl. Sci. 2023, 13(24), 13093; https://doi.org/10.3390/app132413093 - 8 Dec 2023
Viewed by 1208
Abstract
Pulsed thermography is an established nondestructive evaluation technology that excels at detecting and characterizing subsurface defects within specimens. A critical challenge in this domain is the accurate estimation of defect depth. In this paper, a new publicly accessible pulsed infrared dataset for PVC [...] Read more.
Pulsed thermography is an established nondestructive evaluation technology that excels at detecting and characterizing subsurface defects within specimens. A critical challenge in this domain is the accurate estimation of defect depth. In this paper, a new publicly accessible pulsed infrared dataset for PVC specimens is introduced. It was enriched with 3D positional information to advance research in this area. To ensure the labeling quality, a comparative analysis of two distinct data labeling methods was conducted. The first method is based on human domain expertise, while the second method relies on 3D CAD images. The analysis showed that the CAD-based labeling method noticeably enhanced the precision of defect dimension quantification. Additionally, a sophisticated deep learning model was employed on the data, which were preprocessed by different methods to predict both the two-dimensional coordinates and the depth of the identified defects. Full article
(This article belongs to the Special Issue Applied Artificial Intelligence for Industrial Nondestructive Evaluation NDE4.0)
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14 pages, 2845 KiB  
Article
Pulsed Thermography Dataset for Training Deep Learning Models
by Ziang Wei, Ahmad Osman, Bernd Valeske and Xavier Maldague
Appl. Sci. 2023, 13(5), 2901; https://doi.org/10.3390/app13052901 - 24 Feb 2023
Cited by 8 | Viewed by 2605
Abstract
Pulsed thermography is an indispensable tool in the field of non-destructive evaluation. However, the data generated by this technique can be challenging to analyze and require expertise to interpret. With the rapid progress in deep learning, image segmentation has become a well-established area [...] Read more.
Pulsed thermography is an indispensable tool in the field of non-destructive evaluation. However, the data generated by this technique can be challenging to analyze and require expertise to interpret. With the rapid progress in deep learning, image segmentation has become a well-established area of research. This has motivated efforts to apply deep learning methods to non-destructive evaluation data processing, including pulsed thermography. Despite this trend, there has been a lack of public pulsed thermography datasets available for the evaluation of various spatial-temporal deep learning models for segmentation tasks. This paper aims to address this gap by presenting the PVC-Infrared dataset for deep learning. In addition, we evaluated the performance of popular deep-learning-based instance segmentation models on this dataset. Furthermore, we examined the effect of the number of frames and data transformations on the performance of these models. The results of this study suggest that appropriate preprocessing techniques can significantly reduce the size of the data while maintaining the performance of deep learning models, thereby speeding up the data processing process. This highlights the potential for using deep learning methods to make non-destructive evaluation data analysis more efficient and accessible to a wider range of practitioners. Full article
(This article belongs to the Special Issue Applied Artificial Intelligence for Industrial Nondestructive Evaluation NDE4.0)
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