Dimension Estimation of Rectangular Cracks Using Impedance Changes of the Eddy Current Probe with a Neural Network

Babaei, A.; Suratgar, Amir Abolfazl; Salemi, A.H.

doi:10.1016/s1665-6423(13)71549-9

Cited by 10 publications

(5 citation statements)

References 7 publications

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“…Alternatively, to achieve an acceptable accuracy, the population size must be increased what induce a significant computation time. As a result, the proposed method is more preferred; because it is faster and its performance does not change when the calculation is reset, which is not the case for the other stochastic methods [17,18]. Therefore, the following figure summarizes the different steps for 3D crack reconstruction.…”

Section: Beginmentioning

confidence: 99%

3D surface crack characterization by eddy current array image and a fast algorithm search

Abdou

Bouchala

Abdelhadi

et al. 2023

Journal of Electrical Engineering

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Nowadays, 3D eddy current nondestructive characterization of crack and corrosion defects while using ECA remains an industrial challenge because the obtained image permits to determine only the 2D defect shape. Consequently, this article is devoted to determine directly the crack length and width by eddy current images through sensor array. Afterwards, we extract the maximal impedance amplitude to estimate the crack depth while using the deterministic algorithm that we have recently developed. In fact, the obtained results have demonstrated the effectiveness and the reliability of the proposed method.

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Section: Beginmentioning

confidence: 99%

3D surface crack characterization by eddy current array image and a fast algorithm search

Abdou

Bouchala

Abdelhadi

et al. 2023

Journal of Electrical Engineering

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“…The ANN has poor generalization ability outside the range Babaei et al 144 FEM/ANN Calculating the dimension of rectangular cracks…”

Section: Fundingmentioning

confidence: 99%

Challenges in improving the performance of eddy current testing: Review

Abdalla

Faraj

Samsuri

et al. 2018

Measurement and Control

111

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Eddy current testing plays an important role in numerous industries, particularly in material coating, nuclear and oil and gas. However, the eddy current testing technique still needs to focus on the details of probe structure and its application. This paper presents an overview of eddy current testing technique and the probe structure design factors that affect the accuracy of crack detection. The first part focuses on the development of different types of eddy current testing probes and their advantages and disadvantages. A review of previous studies that examined testing samples, eddy current testing probe structures and a review of factors contributing to eddy current signals is also presented. The second part mainly comprised an in-depth discussion of the lift-off effect with particular consideration of ensuring that defects are correctly measured, and the eddy current testing probes are optimized. Finally, a comprehensive review of previous studies on the application of intelligent eddy current testing crack detection in non destructive eddy current testing is presented.

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“…However, this technique presents a blind zone for the acoustic field, located near the sensor, making impossible the detection of surface flaws, if no specific coupling techniques are used [8]. Another well-known NDT technique is EC testing [9][10][11]. This method is used to detect flaws close to the inspected surface as opposed to US.…”

Section: Introductionmentioning

confidence: 99%

A Data Fusion Method for Non-Destructive Testing by Means of Artificial Neural Networks

Cormerais

Duclos²,

Wasselynck³

et al. 2021

Sensors

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In the aeronautics sector, aircraft parts are inspected during manufacture, assembly and service, to detect defects eventually present. Defects can be of different types, sizes and orientations, appearing in materials presenting a complex structure. Among the different inspection techniques, Non Destructive Testing (NDT) presents several advantages as they are noninvasive and cost effective. Within the NDT methods, Ultrasonic (US) waves are widely used to detect and characterize defects. However, due the so-called blind zone, they cannot be easily employed for defects close to the surface being inspected. On the other hand, another NDT technique such Eddy Current (EC) can be used only for detecting flaws close to the surface, due to the presence of the EC skin effect. The work presented in this article aims to combine the use of these two NDT methods, exploiting their complementary advantages. To reach this goal, a data fusion method is developed, by using Machine Learning techniques such as Artificial Neural Networks (ANNs). A simulated training database involving simulations of US and EC signals propagating in an Aluminum block in the presence of Side Drill Holes (SDHs) has been implemented, to train the ANNs. Measurements have been then performed on an Aluminum block, presenting tree different SDHs at specific depths. The trained ANNs were used to characterize the different real SDHs, providing an experimental validation. Eventually, particular attention has been addressed to the estimation errors corresponding to each flaw. Experimental results will show that depths and radii estimations error were confined on average within a range of 4%, recording a peak of 11% for the second SDHs.

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Dimension Estimation of Rectangular Cracks Using Impedance Changes of the Eddy Current Probe with a Neural Network

Cited by 10 publications

References 7 publications

3D surface crack characterization by eddy current array image and a fast algorithm search

3D surface crack characterization by eddy current array image and a fast algorithm search

Challenges in improving the performance of eddy current testing: Review

A Data Fusion Method for Non-Destructive Testing by Means of Artificial Neural Networks

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