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Computational Mathematics and Soft Computing Techniques for Non-destructive Testing and Evaluation (NDT/NDE) and Data Fusion

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A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Computational and Applied Mathematics".

Deadline for manuscript submissions: closed (30 November 2022) | Viewed by 15484

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Dr. Mario Versaci

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Dipartimento di Ingegneria Civile Energia Ambiente e Materiali (DICEAM), “Mediterranea” University, 89122 Reggio Calabria, Italy
Interests: magnetorheological fluids; theoretical models for magnetorheological fluids; experimental models for magnetorheological fluids; magnetorheological fluids for industrial applications
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Dear Colleagues,

There is no doubt that, in the industrial field, non-destructive tests are of fundamental importance as they evaluate the quality of a product without compromising its functionality and/or integrity. Furthermore, the large amount of data obtained from different types of analysis requires the use of data fusion techniques capable of manipulating highly heterogeneous data. In this context, the cooperation among universities and industries has allowed the development and validation of analytical-numerical procedures capable of managing industrial protocols of non-destructive controls and data fusion. However, industrially, the production of data to be analyzed is not free from uncertainties and/or inaccuracies for which it is imperative to use specific techniques, such as soft computing procedures, capable of manipulating data affected by uncertainty and/or imprecision. This Special Issue aims to explore, from a broad perspective, the most recent developments in the field of computational modeling for problems of interest in non-destructive testing and data fusion. Topics of interest range from analytical, numerical, and soft computing modeling techniques to solving these problems.

Prof. Dr. Mario Versaci
Guest Editor

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Keywords

NdT/NdE techniques
Artificial Intelligence
Machine learning
Soft computing techniques
Fuzzy logic and systems
Neural Networks
Numerical techniques Mathematical modeling Physics-based modeling

Published Papers (5 papers)

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Research

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14 pages, 3546 KiB

Open AccessArticle

Proposing an Intelligent Dual-Energy Radiation-Based System for Metering Scale Layer Thickness in Oil Pipelines Containing an Annular Regime of Three-Phase Flow

by Osman Taylan, Mona Abusurrah, Saba Amiri, Ehsan Nazemi, Ehsan Eftekhari-Zadeh and Gholam Hossein Roshani

Mathematics 2021, 9(19), 2391; https://0-doi-org.brum.beds.ac.uk/10.3390/math9192391 - 26 Sep 2021

Cited by 12 | Viewed by 1723

Abstract

Deposition of scale layers inside pipelines leads to many problems, e.g., reducing the internal diameter of pipelines, damage to drilling equipment because of corrosion, increasing energy consumption because of decreased efficiency of equipment, and shortened life, etc., in the petroleum industry. Gamma attenuation could be implemented as a non-invasive approach suitable for determining the mineral scale layer. In this paper, an intelligent system for metering the scale layer thickness independently of each phase’s volume fraction in an annular three-phase flow is presented. The approach is based on the use of a combination of an RBF neural network and a dual-energy radiation detection system. Photo peaks of ²⁴¹Am and ¹³³Ba registered in the two transmitted detectors, and scale-layer thickness of the pipe were considered as the network’s input and output, respectively. The architecture of the presented network was optimized using a trial-and-error method. The regression diagrams for the testing set were plotted, which demonstrate the precision of the system as well as correction. The MAE and RMSE of the presented system were 0.07 and 0.09, respectively. This novel metering system in three-phase flows could be a promising and practical tool in the oil, chemical, and petrochemical industries. Full article

(This article belongs to the Special Issue Computational Mathematics and Soft Computing Techniques for Non-destructive Testing and Evaluation (NDT/NDE) and Data Fusion)

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15 pages, 19344 KiB

Open AccessArticle

Frequency Domain Feature Extraction Investigation to Increase the Accuracy of an Intelligent Nondestructive System for Volume Fraction and Regime Determination of Gas-Water-Oil Three-Phase Flows

by Osman Taylan, Mohammad Amir Sattari, Imene Elhachfi Essoussi and Ehsan Nazemi

Mathematics 2021, 9(17), 2091; https://0-doi-org.brum.beds.ac.uk/10.3390/math9172091 - 29 Aug 2021

Cited by 32 | Viewed by 3502

Abstract

In this research, a methodology consisting of an X-ray tube, one Pyrex-glass pipe, and two NaI detectors was investigated to determine the type of flow regimes and volume fractions of gas-oil-water three-phase flows. Three prevalent flow patterns—namely annular, stratified, and homogenous—in various volume percentages—10% to 80% with the step of 10%—were simulated by MCNP-X code. After simulating all the states and collecting the signals, the Fast Fourier Transform (FFT) was used to convert the data to the frequency domain. The first and second dominant frequency amplitudes were extracted to be used as the inputs of neural networks. Three Radial Basis Function Neural Networks (RBFNN) were trained for determining the type of flow regimes and predicting gas and water volume fractions. The correct detection of all flow regimes and the determination of volume percentages with a Mean Relative Error (MRE) of less than 2.02% shows that the use of frequency characteristics in determining these important parameters can be very effective. Although X-ray radiation-based two-phase flowmeters have a lot of advantages over the radioisotope-based ones, they suffer from lower measurement accuracy. One reason might be that the X-ray multi-energy spectrum recorded in the detector has been analyzed in a simple way. It is worth mentioning that the X-ray sources generate multi-energy photons despite radioisotopes that generate single energy photons, therefore data analyzing of radioisotope sources would be easier than X-ray ones. As mentioned, one of the problems researchers have encountered is the lower measurement accuracy of the X-ray, radiation-based three-phase flowmeters. The aim of the present work is to resolve this problem by improving the precision of the X-ray, radiation-based three-phase flowmeter using artificial neural network (ANN) and feature extraction techniques. Full article

(This article belongs to the Special Issue Computational Mathematics and Soft Computing Techniques for Non-destructive Testing and Evaluation (NDT/NDE) and Data Fusion)

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16 pages, 1906 KiB

Open AccessArticle

Machine Learning-Based Detection Technique for NDT in Industrial Manufacturing

by Alessandro Niccolai, Davide Caputo, Leonardo Chieco, Francesco Grimaccia and Marco Mussetta

Mathematics 2021, 9(11), 1251; https://0-doi-org.brum.beds.ac.uk/10.3390/math9111251 - 29 May 2021

Cited by 21 | Viewed by 5110

Abstract

Fluorescent penetrant inspection (FPI) is a well-assessed non-destructive test method used in manufacturing for detecting cracks and other flaws of the product under test. This is a critical phase in the mechanical and aerospace industrial sector. The purpose of this work was to present the implementation of an automated inspection system, developing a vision-based expert system to automate the inspection phase of the FPI process in an aerospace manufacturing line. The aim of this process was to identify the defectiveness status of some mechanical parts by the means of images. This paper will present, test and compare different machine learning architectures to perform the automated defect detection on a given dataset. For each test sample, several images at different angles were captured to properly populate the input dataset. In this way, the defectiveness status should be found combining the information contained in all the pictures. In particular, the system was designed for increasing the reliability of the evaluations performed on the airplane part, by implementing proper artificial intelligence (AI) techniques to reduce current human operators’ effort. The results show that, for applications in which the dataset available is quite small, a well-designed feature extraction process before the machine learning classifier is a very important step for achieving high classification accuracy. Full article

(This article belongs to the Special Issue Computational Mathematics and Soft Computing Techniques for Non-destructive Testing and Evaluation (NDT/NDE) and Data Fusion)

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14 pages, 13623 KiB

Open AccessArticle

Early Detection of Defects through the Identification of Distortion Characteristics in Ultrasonic Responses

by Pietro Burrascano and Matteo Ciuffetti

Mathematics 2021, 9(8), 850; https://0-doi-org.brum.beds.ac.uk/10.3390/math9080850 - 13 Apr 2021

Cited by 7 | Viewed by 1560

Abstract

Ultrasonic techniques are widely used for the detection of defects in solid structures. They are mainly based on estimating the impulse response of the system and most often refer to linear models. High-stress conditions of the structures may reveal non-linear aspects of their behavior caused by even small defects due to ageing or previous severe loading: consequently, models suitable to identify the existence of a non-linear input-output characteristic of the system allow to improve the sensitivity of the detection procedure, making it possible to observe the onset of fatigue-induced cracks and/or defects by highlighting the early stages of their formation. This paper starts from an analysis of the characteristics of a damage index that has proved effective for the early detection of defects based on their non-linear behavior: it is based on the Hammerstein model of the non-linear physical system. The availability of this mathematical model makes it possible to derive from it a number of different global parameters, all of which are suitable for highlighting the onset of defects in the structure under examination, but whose characteristics can be very different from each other. In this work, an original damage index based on the same Hammerstein model is proposed. We report the results of several experiments showing that our proposed damage index has a much higher sensitivity even for small defects. Moreover, extensive tests conducted in the presence of different levels of additive noise show that the new proposed estimator adds to this sensitivity feature a better estimation stability in the presence of additive noise. Full article

(This article belongs to the Special Issue Computational Mathematics and Soft Computing Techniques for Non-destructive Testing and Evaluation (NDT/NDE) and Data Fusion)

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Review

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11 pages, 1436 KiB

Open AccessReview

An Overview of Non-Destructive Testing of Goss Texture in Grain-Oriented Magnetic Steels

by Antonio Faba and Simone Quondam Antonio

Mathematics 2021, 9(13), 1539; https://0-doi-org.brum.beds.ac.uk/10.3390/math9131539 - 01 Jul 2021

Cited by 4 | Viewed by 2483

Abstract

Grain oriented steels are widely used for electrical machines and components, such as transformers and reactors, due to their high magnetic permeability and low power losses. These outstanding properties are due to the crystalline structure known as Goss texture, obtained by a suitable process that is well-known and in widespread use among industrial producers of ferromagnetic steel sheets. One of the most interesting research areas in this field has been the development of non-destructive methods for the quality assessment of Goss texture. In particular, the study of techniques that can be implemented in industrial processes is very interesting. Here, we provide an overview of techniques developed in the past, novel approaches recently introduced, and new perspectives. The reliability and accuracy of several methods and equipment are presented and discussed. Full article

(This article belongs to the Special Issue Computational Mathematics and Soft Computing Techniques for Non-destructive Testing and Evaluation (NDT/NDE) and Data Fusion)

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