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Applied Sciences
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Progress in Nondestructive Testing and Evaluation (NDT&E)
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Progress in Nondestructive Testing and Evaluation (NDT&E)

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

Deadline for manuscript submissions: 20 June 2025 | Viewed by 7517

Special Issue Editors

Dr. Shiwei Liu

E-Mail Website
Guest Editor
College of Engineering, Huazhong Agricultural University, Wuhan 430070, China
Interests: nondestructive testing and evaluation; structural health monitoring (SHM); digital signal processing; machine learning algorithms; artificial intelligence and optimization methods; finite element modeling; NDT sensor and equipment
Special Issues, Collections and Topics in MDPI journals
Dr. Bo Feng

E-Mail Website
Guest Editor
School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Interests: nondestructive testing methods and instruments; signal processing techniques; machine learning methods; sensors and actuators
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The non-destructive testing and evaluation (NDT&E) technique has been driven by advanced sensors, signal processing methods, material, artificial intelligence, smart sensing and various applications, which is also playing an important role in applied sciences. However, the challenges brought by the deep explorations for theoretical and physical mechanisms of the NDT&E method and sophisticated applications make the NDT&E develop faster than that in any of the recent decades. Therefore, this Special Issue is intended for the presentation of new ideas, experimental results and the latest progress in the field of NDT&E from theory to its practical use.

Areas relevant to the progress of NDT&E include, but are not limited to, reviewing non-destructive testing and evaluation (NDT&E), advanced numerical modeling and simulation method, electromagnetic and optical testing, ultrasonic testing, sensor and actuator network, intelligent perception, signal processing, infrared thermography, machine vision and imaging technique, machine learning and deep learning neural network, and NDT&E applications in industrial, agricultural, medical and aircraft settings. The newest material evaluation and prediction method, multi-objective optimization, complex nonlinear system, and mathematical modeling methods are also topics of interest.

This Special Issue invites authors to submit their high-quality research articles that cover different topics of NDT&E progress. Subjects that will be discussed in this Special Issue focus not only on the newest methods, technologies and applications, but also on the progressive results of their future work.

Dr. Shiwei Liu
Dr. Bo Feng
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

  • non-destructive testing and evaluation (NDT&E)
  • advanced numerical modeling and simulation method
  • electromagnetic testing
  • sensor and actuator
  • intelligent perception
  • signal processing
  • imaging technique
  • machine learning and deep learning
  • NDT&E applications

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Published Papers (5 papers)

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Research

17 pages, 12556 KiB  
Article
Lateral Heat Distribution Characteristics of CLP S275 Using Gaussian FFT Algorithm in Optical Thermographic Testing
by Seungju Lee, Yoonjae Chung, Wontae Kim and Hyunkyu Suh
Appl. Sci. 2024, 14(9), 3776; https://doi.org/10.3390/app14093776 - 28 Apr 2024
Viewed by 923
Abstract
In general, when using infrared thermography (IRT) techniques to excite a heat source on the surface of an inspection object, the heat source is focused on the center of the image of the infrared (IR) camera. If the object to be inspected is [...] Read more.
In general, when using infrared thermography (IRT) techniques to excite a heat source on the surface of an inspection object, the heat source is focused on the center of the image of the infrared (IR) camera. If the object to be inspected is small, uniform excitation of the heat source is possible, but if the area is large, the heat source is concentrated locally, resulting in uneven heat distribution. Therefore, in this study, heat distribution was analyzed after inducing a non-uniform heat source by exciting the heat source at different locations. Additionally, the fast Fourier transform (FFT) algorithm with Gaussian filtering was applied to resolve the non-uniform distribution of the heat sources. Excellent results were obtained from the amplitude image, and the effectiveness of the FFT algorithm was verified using the Otsu algorithm. Finally, the signal-to-noise ratio (SNR) was calculated, and the detection ability according to each thinning rate was analyzed. Full article
(This article belongs to the Special Issue Progress in Nondestructive Testing and Evaluation (NDT&E))
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16 pages, 5150 KiB  
Article
Research on the Application of THz-TDS in Coal–Rock Interface Recognition
by Zichao Jiang, Tianhua Meng, Chunhua Yang, Lei Huang, Hongmei Liu and Weidong Hu
Appl. Sci. 2024, 14(4), 1431; https://doi.org/10.3390/app14041431 - 9 Feb 2024
Viewed by 1126
Abstract
The recognition of coal–rock interface is very crucial for research in the intelligent production of coal mines. To this end, the study investigated the application of terahertz time-domain spectroscopy in the recognition of coal–rock interface, including the identification of coal–rock and coal–rock mixtures, [...] Read more.
The recognition of coal–rock interface is very crucial for research in the intelligent production of coal mines. To this end, the study investigated the application of terahertz time-domain spectroscopy in the recognition of coal–rock interface, including the identification of coal–rock and coal–rock mixtures, as well as the accurate characterization of coal seam thickness. Terahertz detection was used to obtain the optical parameter information of pressed pellets prepared by mixing two different kinds of coal and two kinds of rock. Based on the experiment’s results, a database was established for the identification of coal–rock interfaces for coal mining machines. The terahertz detection was performed on 10 different kinds of sheet anthracite with different thicknesses, and the terahertz spectra of coal seams with different thicknesses were simulated and calculated using simulation software. By comparing the two effective mining thicknesses, parameters can be provided for coal seam mining. The experiment and simulation show that the terahertz time-domain spectroscopy technology has a promising application prospect in the identification of coal–rock interface. Full article
(This article belongs to the Special Issue Progress in Nondestructive Testing and Evaluation (NDT&E))
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15 pages, 6240 KiB  
Article
Development and Investigation of Non-Destructive Detection Drive Mechanism for Precision Type Cylindrical Roller Dynamic Unbalance
by Shijie Liang, Mingde Duan, Zhuangya Zhang, Sier Deng and Fenwu Gao
Appl. Sci. 2023, 13(24), 13266; https://doi.org/10.3390/app132413266 - 15 Dec 2023
Cited by 1 | Viewed by 894
Abstract
In the process of dynamic unbalance detection for precision cylindrical rollers, challenges such as difficulty in achieving effective driving and susceptibility to surface damage during driving significantly impact the accuracy of unbalance detection. This hinders the industry’s ability to achieve the non-destructive detection [...] Read more.
In the process of dynamic unbalance detection for precision cylindrical rollers, challenges such as difficulty in achieving effective driving and susceptibility to surface damage during driving significantly impact the accuracy of unbalance detection. This hinders the industry’s ability to achieve the non-destructive detection of cylindrical rollers. Therefore, this paper proposes a novel driving method to enable non-destructive driving of precision cylindrical rollers. The structural principles of the driving mechanism are presented, and a mechanical model for the cylindrical roller is established to analyze the force distribution. Subsequently, a mathematical model for the air film bearing the cylindrical roller is developed to study the variation characteristics of the air film’s load-bearing capacity. The optimal air film thickness is determined, and the rationality of the mathematical model is validated through simulation analysis. Finally, an experimental platform for non-destructive driving is constructed to further verify the effectiveness of the proposed method. This research provides a prerequisite for the non-destructive detection of dynamic unbalance in precision cylindrical rollers. Full article
(This article belongs to the Special Issue Progress in Nondestructive Testing and Evaluation (NDT&E))
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18 pages, 8907 KiB  
Article
Effective Fabric Defect Detection Model for High-Resolution Images
by Long Li, Qi Li, Zhiyuan Liu and Lin Xue
Appl. Sci. 2023, 13(18), 10500; https://doi.org/10.3390/app131810500 - 20 Sep 2023
Cited by 7 | Viewed by 2236
Abstract
The generation of defects during fabric production impacts fabric quality, and fabric production factories can greatly benefit from the automatic detection of fabric defects. Although object detection algorithms based on convolutional neural networks can be quickly developed, fabric defect detection encounters several problems. [...] Read more.
The generation of defects during fabric production impacts fabric quality, and fabric production factories can greatly benefit from the automatic detection of fabric defects. Although object detection algorithms based on convolutional neural networks can be quickly developed, fabric defect detection encounters several problems. Accordingly, a fabric defect detection model based on Cascade R-CNN was proposed in this study. We also proposed block recognition and detection box merging algorithms to achieve complete defect detection in high-resolution images. We implemented a Switchable Atrous Convolution layer to enhance the feature extraction ability of ResNet-50 and upgraded the Feature Pyramid Network to improve the detection accuracy of small defects. Experimental results demonstrated that the proposed model can efficiently perform defect detection in fabric. Full article
(This article belongs to the Special Issue Progress in Nondestructive Testing and Evaluation (NDT&E))
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13 pages, 4763 KiB  
Article
Comparative Analysis of Discrete Subtraction and Cross-Correlation for Subpixel Object Tracking
by Belén Ferrer, María-Baralida Tomás, Min Wan, John T. Sheridan and David Mas
Appl. Sci. 2023, 13(14), 8271; https://doi.org/10.3390/app13148271 - 17 Jul 2023
Cited by 4 | Viewed by 1277
Abstract
Many applications in physics and engineering require non-invasive, precise object tracking, which can be achieved with image processing methods at very good cost-efficiency ratios. The traditional method for measuring displacement with subpixel resolution involves cross-correlation between images and interpolation of the correlation peak. [...] Read more.
Many applications in physics and engineering require non-invasive, precise object tracking, which can be achieved with image processing methods at very good cost-efficiency ratios. The traditional method for measuring displacement with subpixel resolution involves cross-correlation between images and interpolation of the correlation peak. While this method enables target tracking with a resolution of thousandths of a pixel, it is computationally intensive and susceptible to peak-locking errors. Recently, a new method based on discrete subtraction between images has been presented as an alternative to cross-correlation to improve computational efficiency, which also results in being free of peak-locking errors. This manuscript presents an experimental evaluation of the performance of the discrete subtraction method (DSM) and compares it with the cross-correlation method in terms of subpixel accuracy and deviation errors. Four different targets were used with apparent displacements as small as 0.002 px, which approaches the theoretical digital resolution limit. The results show that the discrete subtraction method is more sensitive to noise but does not suffer from peak-locking error, thus being a reliable alternative to the correlation method, mainly for calibration processes. Full article
(This article belongs to the Special Issue Progress in Nondestructive Testing and Evaluation (NDT&E))
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