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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 Non- Destructive 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.

In this letter, a multifunction aperture array is proposed for ultrawideband (UWB) scanning and polarization reconfiguration. The UWB array consisted of linearly polarized elements is capable of operating in four polarization modes (+45° linear polarization (LP), -45° linear polarization, left-hand circular polarization (LHCP) and right-hand circular polarization (RHCP)). This work involves two essential techniques: (a) A new beam-scanning UWB array synthesis approach. An iterative convex optimization strategy is utilized to determine the element locations and obtain the minimum sidelobe level (SLL) for multiple patterns. (b) The polarization reconfigurable technique for beam-scannable arrays. In this part, a sequential rotation and excitation compensation (SR-EC) technique provides polarization reconfiguration for a beam-scannable array consisting of linearly polarized elements. A beam-scanning UWB array is designed by using the proposed UWB array synthesis approach and the SR-EC polarization reconfigurable technique. The Feko numerical result shows 0°-60° beam peak steering, a 4:1 bandwidth (1-4 GHz), and fourpolarization reconfigurability.

International audience; 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.

International audience; 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.

International audience; 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.