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Electronics
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Application of Artificial Neural Network in Non-destructive Testing
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Application of Artificial Neural Network in Non-destructive Testing

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Networks".

Deadline for manuscript submissions: closed (15 September 2023) | Viewed by 10999

Special Issue Editor

Dr. Gholam Hossein Roshani

E-Mail Website
Guest Editor
Electrical Engineering Department, Kermanshah University of Technology, Kermanshah 6715685420, Iran
Interests: data analysis; artificial neural networks; optimization; modeling and prediction; applied radiation; simulations; signal processing; gamma gauging; multi-phase flow meter; nondestructive tests; radiation detection; cognitive games

Special Issue Information

Dear Colleagues,

Artificial neural networks (ANNs) have a significant role in solving non-destructive testing (NDT) problems. NDT systems, as nonlinear and complex systems, need this efficient tool for obtaining accuracy and better precision. The combination of ANNs with other methods, such as metaheuristic algorithms, feature extraction in time, frequency and time–frequency domains, feature reduction, feature selection, correlation analysis, etc., can improve the efficiency of NDT for obtaining better results for desired purposes.

This Special Issue of Electronics will provide a forum for discussing exciting research on applying AI (especially ANNs) in complex problems of NDT, such as thermography, tomography, multiphase flow metering (MFPM), radiographic testing (RT), electromagnetic testing (ET), visual testing (VT), ultrasonic testing (UT), acoustic emission (AE), shearography testing, etc. Both original research articles and comprehensive review papers are welcome.

Potential topics include, but are not limited to, the following:

  • Artificial intelligence in NDT.
  • ANNs in multiphase flow metering.
  • Advances in the use of signal processing in NDT.
  • Two- and three-phase flows.
  • ANNs in tomography.
  • Complex and nonlinear systems.
  • Increasing the efficiency of NDT.
  • Modeling of flows.
  • The use of soft computing in NDT.
  • Feature extraction, reduction, and selection.

Dr. Gholam Hossein Roshani
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. Electronics 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

  • modeling
  • optimization
  • metaheuristic algorithms
  • computational intelligence
  • non-destructive testing
  • radiographic testing
  • electromagnetic testing
  • visual testing
  • ultrasonic testing
  • acoustic emission
  • artificial neural network
  • artificial intelligence
  • MPFM application

Published Papers (7 papers)

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Research

13 pages, 6429 KiB  
Article
Combination of a Nondestructive Testing Method with Artificial Neural Network for Determining Thickness of Aluminum Sheets Regardless of Alloy’s Type
by Abdulilah Mohammad Mayet, Muhammad Umer Hameed Shah, Robert Hanus, Hassen Loukil, Muneer Parayangat, Mohammed Abdul Muqeet, Ehsan Eftekhari-Zadeh and Ramy Mohammed Aiesh Qaisi
Electronics 2023, 12(21), 4504; https://doi.org/10.3390/electronics12214504 - 2 Nov 2023
Cited by 1 | Viewed by 851
Abstract
Non-destructive and reliable radiation-based gauges have been routinely used in industry to determine the thickness of metal layers. When the material’s composition is understood in advance, only then can the standard radiation thickness meter be relied upon. Errors in thickness measurements are to [...] Read more.
Non-destructive and reliable radiation-based gauges have been routinely used in industry to determine the thickness of metal layers. When the material’s composition is understood in advance, only then can the standard radiation thickness meter be relied upon. Errors in thickness measurements are to be expected in settings where the actual composition of the material may deviate significantly from the nominal composition, such as rolled metal manufacturers. In this research, an X-ray-based system is proposed to determine the thickness of an aluminum sheet regardless of its alloy type. In the presented detection system, an X-ray tube with a voltage of 150 kV and two sodium iodide detectors, a transmission detector and a backscattering detector, were used. Between the X-ray tube and the transmission detector, an aluminum plate with different thicknesses, ranging from 2 to 45 mm, and with four alloys named 1050, 3050, 5052, and 6061 were simulated. The MCNP code was used as a very powerful platform in the implementation of radiation-based systems in this research to simulate the detection structure and the spectra recorded using the detectors. From the spectra recorded using two detectors, three features of the total count of both detectors and the maximum value of the transmission detector were extracted. These characteristics were applied to the inputs of an RBF neural network to obtain the relationship between the inputs and the thickness of the aluminum plate. The trained neural network was able to determine the thickness of the aluminum with an MRE of 2.11%. Although the presented methodology is used to determine the thickness of the aluminum plate independent of the type of alloy, it can be used to determine the thickness of other metals as well. Full article
(This article belongs to the Special Issue Application of Artificial Neural Network in Non-destructive Testing)
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21 pages, 5109 KiB  
Article
Magnetic Flux Leakage Testing Method for Pipelines with Stress Corrosion Defects Based on Improved Kernel Extreme Learning Machine
by Yingqi Li, Chao Sun and Yuechan Liu
Electronics 2023, 12(17), 3707; https://doi.org/10.3390/electronics12173707 - 1 Sep 2023
Cited by 1 | Viewed by 1041
Abstract
This study aims to study the safety of oil and gas pipelines under stress corrosion conditions and grasp the corrosion damage situation timely and accurately. Consequently, a non-destructive testing method combining magnetic flux leakage testing technology and a kernel function extreme learning machine [...] Read more.
This study aims to study the safety of oil and gas pipelines under stress corrosion conditions and grasp the corrosion damage situation timely and accurately. Consequently, a non-destructive testing method combining magnetic flux leakage testing technology and a kernel function extreme learning machine improved by genetic algorithm (GA-KELM) is proposed. Firstly, the variation of the corrosion defect dimension and profile with time is obtained by numerical simulation. At the same time, the distribution of the magnetic flux leakage signal under different defect conditions is analyzed and studied. Finally, feature selection is carried out on the magnetic flux leakage signal distribution curve, and GA-KELM is used to predict the depth and length of corrosion defects so as to realize the non-destructive testing of the pipeline defects. The results show that different geometric features result in different magnetic flux leakage signal distributions. There is a corresponding relationship between the defect dimension and extreme value, area, and peak width of the magnetic flux leakage signal distribution curve. The GA-KELM prediction model can effectively predict the depth and length of corrosion defects, and the prediction accuracy is better than the traditional extreme learning machine prediction model. Full article
(This article belongs to the Special Issue Application of Artificial Neural Network in Non-destructive Testing)
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14 pages, 3148 KiB  
Article
An Experimental and Simulation Study for Comparison of the Sensitivity of Different Non-Destructive Capacitive Sensors in a Stratified Two-Phase Flow Regime
by Mohammad Hossein Shahsavari, Aryan Veisi, Gholam Hossein Roshani, Ehsan Eftekhari-Zadeh and Ehsan Nazemi
Electronics 2023, 12(6), 1284; https://doi.org/10.3390/electronics12061284 - 8 Mar 2023
Cited by 7 | Viewed by 1470
Abstract
Measuring the volume fraction of each phase in multi-phase flows is an essential problem in petrochemical industries. One of the standard flow regimes is stratified two-phase flow, which occurs when two immiscible fluids are present in a pipeline. In this paper, we performed [...] Read more.
Measuring the volume fraction of each phase in multi-phase flows is an essential problem in petrochemical industries. One of the standard flow regimes is stratified two-phase flow, which occurs when two immiscible fluids are present in a pipeline. In this paper, we performed several experiments on vertical concave, horizontal concave, and double-ring sensors to benchmark obtained simulation results from modeling these sensors in stratified two-phase flow using COMSOL Multiphysics software. The simulation data was confirmed by experimental data. Due to the low number of data in the experimental method in order to extract more data, the mentioned software was used to extract more data and then compare the sensitivity of different directions of concave and double ring sensors. The simulation results show that the overall sensitivity of the concave is higher than the double-ring and the momentary sensitivity of the horizontal concave is higher in higher void fractions, and the vertical one has higher sensitivity in lower void fractions. Full article
(This article belongs to the Special Issue Application of Artificial Neural Network in Non-destructive Testing)
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11 pages, 857 KiB  
Article
Improved Artificial Neural Network with High Precision for Predicting Burnout among Managers and Employees of Start-Ups during COVID-19 Pandemic
by Sutrisno Sutrisno, Nurul Khairina, Rahmad B. Y. Syah, Ehsan Eftekhari-Zadeh and Saba Amiri
Electronics 2023, 12(5), 1109; https://doi.org/10.3390/electronics12051109 - 23 Feb 2023
Cited by 5 | Viewed by 1524
Abstract
Notwithstanding the impact that the Coronavirus pandemic has had on the physical and psychological wellness of people, it has also caused a change in the psychological conditions of many employees, particularly among organizations and privately owned businesses, which confronted numerous limitations because of [...] Read more.
Notwithstanding the impact that the Coronavirus pandemic has had on the physical and psychological wellness of people, it has also caused a change in the psychological conditions of many employees, particularly among organizations and privately owned businesses, which confronted numerous limitations because of the unique states of the pandemic. Accordingly, the current review expected to implement an RBF neural network to dissect the connection between demographic variables, resilience, Coronavirus, and burnout in start-ups. The examination technique was quantitative. The statistical populace of the review is directors and representatives of start-ups. In view of the statistical sample size of the limitless community, 384 of them were investigated. For information gathering, standard polls incorporating MBI-GS and BRCS and specialist-made surveys of pressure brought about by Coronavirus were utilized. The validity of the polls was affirmed by a board of specialists and their reliability was affirmed by Cronbach’s alpha coefficient. The designed network structure had ten neurons in the input layer, forty neurons in the hidden layer, and one neuron in the output layer. The amount of training and test data were 70% and 30%, respectively. The output of the neural network and the collected results were compared with each other, and the designed network was able to classify all the data correctly. Using the method presented in this research can greatly help the sustainability of companies. Full article
(This article belongs to the Special Issue Application of Artificial Neural Network in Non-destructive Testing)
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12 pages, 5764 KiB  
Article
Experimental Analysis to Detect Corona COVID-19 Virus Symptoms in Male Patients through Breath Pattern Using Machine Learning Algorithms
by Abdulilah Mohammad Mayet, Neeraj Kumar Shukla, M. Ramkumar Raja, Ijaz Ahmad, Ramy Mohammed Aiesh Qaisi, Ali Awadh Al-Qahtani, Anita Taparwal, Vineet Tirth and Reem AL-Dossary
Electronics 2023, 12(1), 10; https://doi.org/10.3390/electronics12010010 - 20 Dec 2022
Cited by 1 | Viewed by 1502
Abstract
In the fourth quarter of the year 2019, the planet became overwhelmed by the pandemic caused by the coronavirus disease (COVID-19). This virus imperiled human life and have affected a considerable percentage of the world population much before its early stage detection mechanisms [...] Read more.
In the fourth quarter of the year 2019, the planet became overwhelmed by the pandemic caused by the coronavirus disease (COVID-19). This virus imperiled human life and have affected a considerable percentage of the world population much before its early stage detection mechanisms were discovered and made available at the grassroots level. As there is no specific drug available to treat this infection, the vaccine was intended to serve as the ultimate weapon in the war against this species of coronavirus, but like other viruses, being an RNA virus, this virus also mutates continuously while it passes from one human to the other, making the development of highly potent vaccines even more challenging. This work is being sketched at the juncture when a huge percentage of the human population is already affected by this virus globally. In this work, we are proposing an idea to develop an app to detect coronavirus (COVID-19) symptoms at an early stage by self-diagnosis at home or at the clinical level. An experimental study has been performed on a dummy dataset with 11000 entries of various breadth patterns based on the spirometry analysis, lung volume analysis, and lung capacity analysis of normal male subjects and detailed breath patterns of infected male patients. A logistic regression model is trained after using SMOTE oversampling to balance the data and the predictive accuracy levels of 80%, 78%, and 90%. The results accomplished through this study and experiments may not only aid the clinicians in their medical practice but may also bestow a blue chip to the masterminds engaged in the biomedical research for inventing more evolved, sophisticated, user-friendly, miniaturized, portable, and economical medical app/devices in the future. Full article
(This article belongs to the Special Issue Application of Artificial Neural Network in Non-destructive Testing)
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17 pages, 2920 KiB  
Article
A New Design Method for Class-E Power Amplifiers Using Artificial Intelligence Modeling for Wireless Power Transfer Applications
by Salah I. Yahya, Ban M. Alameri, Mohammad (Behdad) Jamshidi, Saeed Roshani, Muhammad Akmal Chaudhary, Gerald K. Ijemaru, Yaqeen Sabah Mezaal and Sobhan Roshani
Electronics 2022, 11(21), 3608; https://doi.org/10.3390/electronics11213608 - 4 Nov 2022
Cited by 12 | Viewed by 2514
Abstract
This paper presents a new approach to simplify the design of class-E power amplifier (PA) using hybrid artificial neural-optimization network modeling. The class-E PA is designed for wireless power transfer (WPT) applications to be used in biomedical or internet of things (IoT) devices. [...] Read more.
This paper presents a new approach to simplify the design of class-E power amplifier (PA) using hybrid artificial neural-optimization network modeling. The class-E PA is designed for wireless power transfer (WPT) applications to be used in biomedical or internet of things (IoT) devices. Artificial neural network (ANN) models are combined with optimization algorithms to support the design of the class-E PA. In several amplifier circuits, the closed form equations cannot be extracted. Hence, the complicated numerical calculations are needed to find the circuit elements values and then to design the amplifier. Therefore, for the first time, ANN modeling is proposed in this paper to predict the values of the circuit elements without using the complex equations. In comparison with the other similar models, high accuracy has been obtained for the proposed model with mean absolute errors (MAEs) of 0.0110 and 0.0099, for train and test results. Moreover, root mean square errors (RMSEs) of 0.0163 and 0.0124 have been achieved for train and test results for the proposed model. Moreover, the best and the worst-case related errors of 0.001 and 0.168 have been obtained, respectively, for the both design examples at different frequencies, which shows high accuracy of the proposed ANN design method. Finally, a design of class-E PA is presented using the circuit elements values that, first, extracted by the analyses, and second, predicted by ANN. The calculated drain efficiencies for the designed class-E amplifiers have been obtained equal to 95.5% and 91.2% by using analyses data and predicted data by proposed ANN, respectively. The comparison between the real and predicted values shows a good agreement. Full article
(This article belongs to the Special Issue Application of Artificial Neural Network in Non-destructive Testing)
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12 pages, 4435 KiB  
Article
Application of Nondestructive Techniques to Investigate Dissolvable Amorphous Metal Tungsten Nitride for Transient Electronics and Devices
by Abdulilah Mohammad Mayet, Mohammed Abdul Muqeet, Ali Awadh Alqahtani, Muhammad Abbas Khan, Abdulrahim Othman Dawbi, Hala H. Alhashim, Ramy Mohammed Aiesh Qaisi, Nivin A. Ghamry and Elsayed M. Tag-Eldin
Electronics 2022, 11(20), 3284; https://doi.org/10.3390/electronics11203284 - 12 Oct 2022
Viewed by 1118
Abstract
Transient electronics can be gradually dissolved in a variety of liquids over time. The short-lived nature of such electronics has promoted their implementation in prospective applications, such as implantable electronics, dissolvable devices for secure systems, and environmentally biodegradable electronics. The amorphous metal tungsten [...] Read more.
Transient electronics can be gradually dissolved in a variety of liquids over time. The short-lived nature of such electronics has promoted their implementation in prospective applications, such as implantable electronics, dissolvable devices for secure systems, and environmentally biodegradable electronics. The amorphous metal tungsten nitride (WNx) has the remarkable ability to scale down to the nano-scale, allowing the fabrication of sub-1 volt nano-electromechanical (NEM) switches. When compared to silicon, amorphous WNx has a greater density and electrical conductivity, making it an even more appealing material for the design of accelerometers and resistive temperature detectors. Kinetic hydrolysis is observed by the dissolution of amorphous WNx in ground water. To better understand the kinetics of hydrolysis, in this paper, samples are dissolved in different solutions under different conditions over time. NEM switches immersed in ground water, de-ionized (DI) water, and salty water are subjected to temperatures of 0 °C (degrees Celsius), 25 °C (room temperature, RT), and 60 °C. Sonicated samples are tested at both room temperature (RT) and at 60 °C. During the course of dissolving, the resistivity of amorphous WNx is measured, and an increase in resistance is noted when the thickness of the amorphous WNx is reduced. The wettability of a solid can be easily determined by measuring its contact angle, which indicates either the hydrophobic or hydrophilic nature of the surface. The contact angle of the amorphous WNx is measured to be about 30.8°, indicating hydrophilicity. For the temperature sensor characterization, a probe station with a thermal chuck is used to apply heat from the bottom of the sensor. The actual real-time temperature of the amorphous WNx sensor is measured using a thermocouple tip on the surface of the sensor. Full article
(This article belongs to the Special Issue Application of Artificial Neural Network in Non-destructive Testing)
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