─ Industrial structure are exposed to microstructural changes caused by fatigue cracking, corrosion and thermal aging. Generally, a hidden crack is very dangerous because it is difficult to detect by NonDestructive Evaluation (NDE) techniques. This paper presents a new approach to estimate the hidden cracks dimensions inside a stainless steel plate based on the EMAT signal. The received signal by EMAT is simulated using the Finite Element Method (FEM). Then, the identification of the hidden crack sizes is performed via the combination of two techniques; the first one is the Time-of-Flight (ToF) technique which was applied to estimate the crack height by the evaluation of the difference between the ToF of the healthy form and the defective form. Then, the crack width is estimated by the solution of the inverse problem from the received signal based on a meta-heuristic algorithm called Teaching learning Based optimization (TLBO). The obtained results illustrate the sensitivity of the EMAT sensor to the variation of the crack sizes. Moreover, the quantitative evaluation of the cracks dimensions, show clearly the efficiency and reliability of the adopted approache. Index Terms ─ Characterization of hidden cracks, FEM, NDE, Time-of-Flight, TLBO algorithm.

Industrial equipment can be exposed to various types of damage during their long exploitation in harsh environments which might lead to corrosion cracking. Typically, a hidden crack is very dangerous since it is difficult to be detected by Non-Destructive Testing (NDT) techniques. Moreover, the integrity requirements of metallic structures cannot be satisfied by simply detecting the existence of cracks. However, the quantitative description of the cracks is still an issue that should be solved. This paper introduces a new approach to estimate the dimensions of hidden cracks inside a stainless steel plate based on Shear Vertical (SV) waves generated by an Electromagnetic Acoustic Transducer (EMAT). The Finite Elements Method (FEM) has been used to model the acquired signal by EMAT. Then, the characterization of hidden cracks is carried out by combining two methods; Time-of-Flight (ToF) technique that has been used to evaluate the crack height, and the Partial Least Square Regression (PLSR) method that has been used to estimate the crack width. The obtained results demonstrate the efficiency and the reliability of the adopted approach concerning the quantitative evaluation of the crack dimensions.

—The aim of this research is to propose a new efficient and reliable approach on the field of Non Destructive Testing (NDT), for the characterization of cracks in non-ferromagnetic material by Electromagnetic Acoustic Transducer (EMAT). EMAT is an ultrasonic technique that generates and detects ultrasonic waves in the conductive material without physical contact. The research goes through two principal phases. The first, which is a forward model, is based on Finite Element Method (FEM). The FEM is applied to simulate the EMAT response (output voltage) to the material under test in order to build a database for the inversion tool. The second is the inverse model and depends on the Partial Least Square Regression (PLSR) method, as it is a fast, simple, and accurate inversion tool, in order to estimate the depth and width of the cracks on the surface of non-ferromagnetic materials. PLSR is a dimensionality reduction method which aims to model the relationship between the matrix of independent variables (predictors) ( X ) and the matrix of dependent variables (response) ( Y ). The purpose of PLSR is to find the Latent Variables (LV) that have a higher ability of prediction by projecting original predictors into a new space of reduced dimensions.

Eddy Current Testing (ECT) is a fast and effective method for detecting and sizing most of the default in conducting materials. The size estimation of an unknown defect from the measurement of the impedance variations is an important technique in industrial area. This paper considers to solve this problem by the novel combination of the Least Square Support Vector Machines (LS-SVM) and Finite Element Method (FEM). The FEM is used to modelling the eddy current sensor. In this context, Comsol Multiphysics resolution using a 3D electromagnetic formulation have been considered to create a database required to train the LS-SVM. Several method exist to find the LS-SVM parameters. Electromagnetism-like Mechanism (EM) algorithm is proposed. A good agreement is obtained between the numerical results and the experimental measure.

Eddy current (EC) sensors are used for non-destructive testing since they are able to probe conductive materials. Despite being a conventional technique for defect detection and localization, the main weakness of this technique is that defect characterization, of the exact determination of the shape and dimension, is still a question to be answered. In this work, we demonstrate the capability of small crack sizing using signals acquired from an EC sensor. We report our effort to develop a systematic approach to estimate the size of rectangular and thin defects (length and depth) in a conductive plate. The achieved approach by the novel combination of a finite element method (FEM) with a statistical learning method is called least square support vector machines (LS-SVM). First, we use the FEM to design the forward problem. Next, an algorithm is used to find an adaptive database. Finally, the LS-SVM is used to solve the inverse problems, creating polynomial functions able to approximate the correlation between the crack dimension and the signal picked up from the EC sensor. Several methods are used to find the parameters of the LS-SVM. In this study, the particle swarm optimization (PSO) and genetic algorithm (GA) are proposed for tuning the LS-SVM. The results of the design and the inversions were compared to both simulated and experimental data, with accuracy experimentally verified. These suggested results prove the applicability of the presented approach.

This paper presents the use of the Least Square Support Vector Machines (LS-SVM) technique, combined with the Finite Element Method (FEM), to characterize small cracks in order to get a fast non-destructive inspection. The LS-SVM is a statistical learning method that has good generalization capability and learning performance. LS-SVM trained model is proposed to predict crack sizing using experimental signals acquired from an Eddy Current (EC) sensor. The FEM is used to create the data set required to train this model. The performance of LS-SVM model depends on a careful setting of its associated hyper-parameters. Different tuning techniques for optimizing the LS-SVM hyper-parameters are studied: Electromagnetism-Like Mechanism (EM), Opposition Based Electromagnetism-Like Mechanism (OBEM) and Teaching Learning Based Optimization (TLBO). Results show that TLBO algorithm provides a good compromise between accuracy and computational cost.

This paper is concerned with the characterization methodologies of defects in conducting materials by an Electromagnetic Acoustic Transducer (EMAT) testing system. It has been developed to create a virtual environment for Non-Destructive Testing (NDT) before implementing it in real, to study the change effect on the defect geometry at the signal received. EMAT is a new technology, which provides a noncontact process of testing materials compared to ultrasonic testing technique. This work is based on the simulation of two-dimensional numerical model, using Finite Element Method, (FEM) like a simulator model forward analysis, which includes the calculation of induced eddy current, the Lorentz force, and mechanical displacement inside conducting material. Results obtained shows that the model is capable of detecting the depth, the width and the location of the surface defect in an Aluminum material, using the mechanical displacement amplitude.

This paper considers the inverse problem of estimating the size of an unknown defect from the measurement of the impedance variations, By the novel combination of the multi output support vector machines (MO-SVM), and finite element modeling for eddy current flaw Characterization of a defect size in a conductive nonmagnetic plate.The finite element method (FEM) is used to create the database, In this context, the cylindrical sensor have been considered to validate a COMSOL-Multi physics 3D-resolution using a 3D electromagnetic formulation , A good agreement is obtained between the numerical results and the experimental ones. The database required to train the MO-SVM , Several method are used to find the parameters of MOSVM. the Particle Swarm Optimization (PSO) ,Genetic Algorithm GA are proposed.