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

Deadline for manuscript submissions: closed (31 January 2024) | Viewed by 11675

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Special Issue Editors

Prof. Dr. Sung-Jin Song

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Guest Editor

School of Mechanical Engineering, College of Engineering, Sungkyunkwan University, Suwon 16419, Korea
Interests: modeling and simulating of nondestructive evaluation; automated NDE Systems; intelligent inversion of NDE signals; nondestructive materials characterization

Prof. Dr. Hak-Joon Kim

E-Mail Website
Guest Editor

Safety and Structural Integrity Research Center, College of Engineering, Sungkyunkwan University, Suwon 16419, Korea
Interests: modeling and mimulating of nondestructive evaluation; signal processing of NDE; machine learning of NDE; nuclear safety diagnosis;

Special Issue Information

Dear Colleagues,

We invite you to contribute to a Special Issue on the topic of recent advances of ultrasonic testing in materials. This Special Issue is about ultrasonic non-destructive testing and the evaluation applied in various research areas and practical fields. In particular, the Special Issue is to share the recent advances in finding defects in high-engineering application materials, such as titanium, Inconel, composite, dissimilar metal welds, TBC coating, etc. In addition, this Special Issue also intends to share the recent advances in ultrasonic nondestructive evaluation of the properies of those materials.

Especially, we encourage you to share your new approaches for inspecting defects that are difficult to detect using currently available techniques in the industrial fields. Those problematic defects would include surface micro cracks and IGSCC (intergranular stress corrosion cracking), etc.

We certainly welcome your new approaches using advanced sensors, such as IDT Sensor, EMAT, and air-cupplied transducer. We especially welcome your cutting-edge techniques using robust signal processing and interpretation tools, such as neural networks and export system.

We look forward to your participation and your expertise from academic and industrial fields.

Prof. Dr. Sung-Jin Song
Prof. Dr. Hak-Joon Kim
Guest Editors

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Keywords

ultrasonic non-destructive testing
ultrasonic non-destructive evaluation
defect characterization in materials
material property characterization
advanced ultrasonic sensors
artificial intelligence
advanced signal processing and interpretation

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Published Papers (4 papers)

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Research

14 pages, 4120 KiB

Open AccessArticle

Extraction of Flaw Signals from the Mixed 1-D Signals by Denoising Autoencoder

by Seung-Eun Lee, Jinhyun Park, Hak-Joon Kim and Sung-Jin Song

Appl. Sci. 2023, 13(6), 3534; https://doi.org/10.3390/app13063534 - 10 Mar 2023

Cited by 3 | Viewed by 1725

Abstract

Ultrasonic testing (UT) is one of the most popular non-destructive evaluation (NDE) techniques used in many industries to evaluate structural integrity. The commonly used NDE techniques are basic inspection techniques, such as visual testing (VT), penetration testing (PT), and magnetic testing (MT), and advanced inspection techniques, such as UT, radiography testing (RT), eddy current testing (ECT), and phased array ultrasonic testing (PAUT). Among the numerous advanced techniques, ultrasonic testing (UT) is usually used for the inspection of welds in various industries. However, the application of UT still has some shortcomings to overcome. One major shortcoming that reduces the precision of UT is the extra signals from the geometrical interface of a specimen. UT uses the reflection indications of the ultrasonic beam. However, the reflection signals from the welding interface and geometry along with the target flaw signal produce mixed signals. The inspectors use a 1-D reflection outcome called the ultrasonic A-scan to evaluate the welding integrity. The mixed ultrasonic A-scan signals are often very difficult to analyze because inspectors must distinguish the target flaw signal of welding from the mixed ultrasonic A-scan signal, which includes the flaw indication as well as the background signal. Therefore, a method to distinguish between the flaw signal and the background signal must be developed for the efficiency of UT. Autoencoder is an artificial neural network that is made for feature extraction from the input. Denoising autoencoder (DAE) is one of the derivative models of the autoencoder which adds or eliminates random noise signals to extract the prominent features. DAE is already widely used in the denoising of images and sound data. The characteristics of DAE are used in this research to distinguish the ultrasonic flaw signal from the mixed ultrasonic A-scan signal. For the training, 2463 mixed A-scan signals were obtained from 45 different standard blocks in which 5 different types of flaws were embedded. For testing, we used 1000 mixed A-scan signals. The performance of the network was evaluated using a point-by-point comparison method. The autoencoder was trained to denoise the background signal from the mixed ultrasonic A-scan, and the target flaw signal was extracted from the original A-scan signal. Full article

(This article belongs to the Special Issue Recent Advances of Ultrasonic Testing in Materials)

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12 pages, 10042 KiB

Open AccessArticle

Detection and Classification of Artificial Defects on Stainless Steel Plate for a Liquefied Hydrogen Storage Vessel Using Short-Time Fourier Transform of Ultrasonic Guided Waves and Linear Discriminant Analysis

by Young-In Hwang, Mu-Kyung Seo, Hyun Geun Oh, Namkyoung Choi, Geonwoo Kim and Ki-Bok Kim

Appl. Sci. 2022, 12(13), 6502; https://doi.org/10.3390/app12136502 - 27 Jun 2022

Cited by 8 | Viewed by 2344

Abstract

Liquefied hydrogen storage vessels (LHSVs) are vulnerable to surface-crack initiation, propagation, and fracture on their surfaces because they are under high-pressure, low-temperature conditions. Defects can also occur in the coatings of the storage containers used to prevent hydrogen permeation, and these lead to surface defects such as pitting corrosions. Together, these increase the probability of liquid hydrogen leaks and can cause serious accidents. Therefore, it is important to detect surface defects during periodic surface inspections of LHSVs. Among the candidate non-destructive evaluation (NDE) techniques, testing using guided waves (GWs) is effective for detecting surface defects. Because of the ability of GWs to travel long distances without significant acoustic attenuation, GW testing has attracted much attention as a promising structural monitoring technique for LHSVs. In this study, an ultrasonic NDE method was designed for detecting surface defects of 304SS plate, which is the main material used for fabricating LHSVs. It involves the use of linear discriminant analysis (LDA) based on short-time Fourier transform (STFT) pixel information produced from GW data. To accomplish this, the differences in the number of STFT pixels between sound and defective specimens were used as a major factor in distinguishing the two groups. Consequently, surface defects could be detected and classified with 97% accuracy by the newly developed pixel-based mapping method. This indicates that the newly developed NDE method with LDA can be used to detect defects and classify LHSVs as either sound or defective. Full article

(This article belongs to the Special Issue Recent Advances of Ultrasonic Testing in Materials)

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12 pages, 4373 KiB

Open AccessArticle

NDE Characterization of Surface Defects on Piston Rods in Shock Absorbers Using Rayleigh Waves

by Kwang-Hee Im, Yun-Taek Yeom, Hyung-Ho Lee, Sun-Kyu Kim, Young-Tae Cho, Yong-Deuck Woo, Peng Zhang, Gui-Lin Zhang and Sung-Duk Kwon

Appl. Sci. 2022, 12(12), 5986; https://doi.org/10.3390/app12125986 - 12 Jun 2022

Cited by 2 | Viewed by 2230

Abstract

In general, shock absorbers are components that can absorb shock and vibration energy caused by wheel behavior, and they provide handling stability. As a piston rod is an important component in shock absorbers, multiple processes are performed in order to guarantee its quality during manufacturing. Micro-defects can be generated on the surfaces of piston rods after processing. Because these defects can degrade the function of shock absorbers, proper non-destructive techniques are necessary to monitor the surfaces of piston rods. In this study, micro-defects were artificially machined on the surfaces of piston rods. In particular, a Rayleigh wave technique was adopted to detect defects on the surfaces of the piston rods, and Rayleigh wave behaviors were analyzed to establish beam profiles. In terms of the experimental method, defects were fabricated on the piston rods, and the optimal Rayleigh angle was determined using the pulse-echo method with ultrasonic transducers in a water tank. This was performed to evaluate the characteristics of the Rayleigh waves. In testing, regardless of the types of micro-defects on the surfaces of the pistons, it was found that the optimal inspection condition could be in the range of 5–10 mm, where ultrasonic signals were received with a high resolution. Moreover, the behaviors of the transmitted Rayleigh waves were simulated, and reflection, transmission, and scattering occurred due to defects at the interface between the water and steel. Thus, the propagation of Rayleigh waves and the optimal test conditions were implemented through FEM simulation to generate effective Rayleigh waves. Full article

(This article belongs to the Special Issue Recent Advances of Ultrasonic Testing in Materials)

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11 pages, 3599 KiB

Open AccessArticle

Automated Classification of Ultrasonic Signal via a Convolutional Neural Network

by Yakun Shi, Wanli Xu, Jun Zhang and Xiaohong Li

Appl. Sci. 2022, 12(9), 4179; https://doi.org/10.3390/app12094179 - 21 Apr 2022

Cited by 15 | Viewed by 3424

Abstract

Ultrasonic signal classification in nondestructive testing is of great significance for the detection of defects. The current methods have mainly utilized low-level handcrafted features based on traditional signal processing approaches, such as the Fourier transform, wavelet transform and the like, to interpret the information carried by signals for classification. This paper proposes an automatic classification method via a convolutional neural network (CNN) which can automatically extract features from raw data to classify ultrasonic signals collected of a circumferential weld composed of austenitic and martensitic stainless steel with internal slots. Experiments demonstrate that our method outperforms the traditional classifier with manually extracted features, achieving an accuracy rate of classification up to 0.982. Furthermore, we visualize the shape, location and orientation of defects with a C-scan imaging process based on classification results, validating the effectiveness of the results. Full article

(This article belongs to the Special Issue Recent Advances of Ultrasonic Testing in Materials)

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