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Ultrasonic Non-destructive Testing: Technologies and Applications
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Ultrasonic Non-destructive Testing: Technologies and Applications

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

Deadline for manuscript submissions: closed (20 November 2023) | Viewed by 2859

Special Issue Editor

Dr. Chenyin Ni

E-Mail Website
Guest Editor
School of Physics, Nanjing University of Science and Technology, Nanjing 210094, China
Interests: non-destructive testing; imaging techniques; laser ultrasonics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to announce a Special Issue of Applied Sciences entitled "Ultrasonic Non-destructive Testing: Technologies and Applications." This Special Issue aims to provide a comprehensive overview of the latest advancements and applications in the field of ultrasonic non-destructive testing.

Ultrasonic non-destructive testing has emerged as a powerful technique for evaluating the integrity, quality, and performance of materials and structures without causing any damage. It offers numerous advantages such as high sensitivity, real-time imaging, and non-invasiveness, making it a preferred choice in various industries including manufacturing, aerospace, civil engineering, and healthcare.

This Special Issue will cover a wide range of topics related to ultrasonic non-destructive testing. We invite researchers, academics, and industry experts to contribute their original research articles, reviews, and case studies including, but not limited to, the following subjects:

  • Advances in ultrasonic transducers and sensors;
  • Novel signal processing and imaging techniques;
  • Ultrasonic guided wave testing for structural health monitoring;
  • Non-destructive evaluation of composite materials using ultrasonic waves;
  • Ultrasonic testing for defect detection and characterization;
  • Applications of ultrasonic testing in additive manufacturing;
  • Ultrasonic testing in the oil and gas industry;
  • Ultrasonic imaging for medical diagnostics;
  • Laser ultrasonics techniques and applications;
  • Simulation and modeling of ultrasonic testing methods;
  • Advances in ultrasonic phased array technology.

We encourage submissions that present innovative methodologies, significant research findings, and practical applications of ultrasonic non-destructive testing. All submitted manuscripts will undergo a rigorous peer-review process to ensure the highest scientific standards.

This Special Issue provides an excellent opportunity to disseminate your research and contribute to the advancement of ultrasonic non-destructive testing. We look forward to receiving your valuable contributions.

Dr. Chenyin Ni
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.

Keywords

  • ultrasonic wave
  • non-destructive testing
  • defect detection
  • diagnostic imaging
  • structural health monitoring
  • signal processing
  • imaging techniques
  • phase array
  • transducers
  • material characterization

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Published Papers (2 papers)

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Research

20 pages, 9259 KiB  
Article
In-Depth Steel Crack Analysis Using Photoacoustic Imaging (PAI) with Machine Learning-Based Image Processing Techniques and Evaluating PAI-Based Internal Steel Crack Feasibility
by Arbab Akbar, Ja Yeon Lee, Jun Hyun Kim and Myung Yung Jeong
Appl. Sci. 2023, 13(24), 13157; https://doi.org/10.3390/app132413157 - 11 Dec 2023
Viewed by 1506
Abstract
Steel plays an indispensable role in our daily lives, permeating various products ranging from essential commodities and recreational gears to information technology devices and general household items. The meticulous evaluation of steel defects holds paramount importance to ensure the secure and dependable operation [...] Read more.
Steel plays an indispensable role in our daily lives, permeating various products ranging from essential commodities and recreational gears to information technology devices and general household items. The meticulous evaluation of steel defects holds paramount importance to ensure the secure and dependable operation of the end products. Photoacoustic imaging (PAI) emerges as a promising modality for structural inspection in the realm of health monitoring applications. This study incorporates PAI experimentation to generate an image dataset and employs machine learning techniques to estimate the length and width of surface cracks. Furthermore, the research delves into the feasibility assessment of employing PAI to investigate internal cracks within a steel sample through a numerical simulation-based study. The study’s findings underscore the efficacy of the PAI in achieving precise surface crack detection, with an acceptable root mean square error (RMSE) of 0.63 ± 0.03. The simulation results undergo statistical analysis techniques, including the analysis of variance (ANOVA) test, to discern disparities between pristine samples and those featuring internal cracks at different locations. The results discern statistically significant distinctions in the simulated acoustic responses for samples with internal cracks of varying sizes at identical/different locations (p < 0.001). These results validate the capability of the proposed technique to differentiate between internal crack sizes and positions, establishing it as a viable method for internal crack detection in steel. Full article
(This article belongs to the Special Issue Ultrasonic Non-destructive Testing: Technologies and Applications)
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17 pages, 4934 KiB  
Article
Assessing Weak Adhesion in Single Lap Joints Using Lamb Waves and Machine Learning Methods for Structural Health Monitoring
by Gabriel M. F. Ramalho, António M. Lopes and Lucas F. M. da Silva
Appl. Sci. 2023, 13(19), 10877; https://doi.org/10.3390/app131910877 - 30 Sep 2023
Cited by 1 | Viewed by 954
Abstract
The use of adhesive joints has become increasingly popular in various industries due to their many benefits, such as low weight and good mechanical performance. However, adhesive joints can suffer from defects, one of them being weak adhesion. This defect poses a significant [...] Read more.
The use of adhesive joints has become increasingly popular in various industries due to their many benefits, such as low weight and good mechanical performance. However, adhesive joints can suffer from defects, one of them being weak adhesion. This defect poses a significant risk to structural integrity and can lead to premature failure, but is hard to detect using existing nondestructive testing methods. Therefore, there is a need for an effective technique that can detect weak adhesion in single-lap joints (SLJ) to prevent failure and assist in maintenance, namely in the framework of structural health monitoring. This paper presents a novel approach utilizing machine learning and Lamb Waves (LW) to determine the level of weak adhesion. Firstly, a numerical model of SLJs with different levels of weak adhesion is created and an original approach is proposed for its validation with data from real samples so that reliable LW data can further be easily generated to train and test any other data-driven algorithm for tackling damage. Secondly, a damage detection method is proposed, based on artificial neural networks and fed with simulated data, to determine the level of damage in SLJs, independent of their location. The results show that the simulation model can be validated with a small set of experimental data, being capable of replicating real damage in SLJs. Additionally, the use of simulated data in the training algorithm can increase the accuracy of the simulation model up to 26% when compared to only considering experimental data. The adopted artificial neural network for detecting weak adhesion emerges as a promising approach, yielding a precision of over 95%. Thus, machine learning and LW data can be used to improve the reliability and accuracy of adhesive bonding quality control, as well function as a technique for structural health monitoring, which can enhance the safety and durability of bonded structures. Full article
(This article belongs to the Special Issue Ultrasonic Non-destructive Testing: Technologies and Applications)
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