Francesco Carlo Morabito

Swarm Optimization for Imaging of Corrosion by Impedance Measurements in Eddy Current Test

Eddy current techniques (ECTs) for nondestructive testing and evaluation (NDT/NDE) of conducting materials is one of most application-oriented field of research within electromagnetics. In this work, quantitative nondestructive evaluation of corrosion in metallic plates is considered. The inspection method exploits impedance measurements to determine the material loss profile caused by corrosion. Considering the corrosion shape as an impedance measurements function, it has to be minimized in order to obtain the corrosion profile. Particularly, swarm intelligence, characterized by evolutionary computation, has been exploited for the purpose

FEA Design and Misfit Minimization for In-Depth Flaw Characterization in Metallic Plates With Eddy Current Nondestructive Testing

Nondestructive testing techniques are more and more exploited in order to quickly and cheaply recognize flaws into the inspected materials. Within this framework, a concern of eddy current tests is the depth of penetration delta, above all in such applications as the control of steel beams. Thus, an optimal design of exciting coil is strictly required in order to reach as higher delta as possible. The aim of this paper is first to design, by finite-element analysis, and test by in-lab measurements, a suitable exciting coil. Subsequently, the inverse ill-posed problem for defect characterization, starting from experimental measurements, has been studied and regularized, in order to characterize the depth and the extension of defects.

Advances in Signal Processing to Reduce Lift-off Noise in Eddy Current Tests

Nowadays, Non Destructive Testing and Evaluation (NDT/NDE) are frequently used to evaluate integrity of manufactured articles in civil or industrial applications. In this framework, Eddy Current Test (ECT) technique has a primary role for inspection of conducting materials. Solution of many related inverse problems requires an accurate comprehension of mea- sured data: it is one of the most challenging problem in this context. Thats why a suitable signal pre-processing is necessary. In this paper, advanced signal processing techniques are evaluated in order to reduce the impact of lift-ofi efiect on the eddy current data. Some hybrid approaches are also depicted, with encouraging results. DOI: 10.2529/PIERS061007215011

Fuzzy similarity measures for detection and classification of defects in CFRP

The systematic use of nondestructive testing assumes a remarkable importance where on-line manufacturing quality control is associated with the maintenance of complex equipment. For this reason, nondestructive testing and evaluation (NDT/NDE), together with accuracy and precision of measurements of the specimen, results as a strategic activity in many fields of industrial and civil interest. It is well known that nondestructive research methodologies are able to provide information on the state of a manufacturing process without compromising its integrity and functionality. Moreover, exploitation of algorithms with a low computational complexity for detecting the integrity of a specimen plays a crucial role in real-time work. In such a context, the production of carbon fiber resin epoxy (CFRP) is a complex process that is not free from defects and faults that could compromise the integrity of the manufactured specimen. Ultrasonic tests provide an effective contribution in identifying the presence of a defect. In this work, a fuzzy similarity approach is proposed with the goal of localizing and classifying defects in CFRP in terms of a sort of distance among signals (measure of ultrasonic echoes). A field-programmable gate array (FPGA)-based board will be also presented which implements the described algorithms on a hardware device. The good performance of the detection and classification achieved assures the comparability of the results with the results obtained using heuristic techniques with a higher computational load.

Evaluation of Defects in Multilayer Carbon Fibre Epoxy for Aeronautics Applications

Production of carbon fibre reinforced polymers is an elaborate process unfree from faults and problems. Problems during the manufacturing, such as plies overlapping, can cause flaws in the resulting material, so compromising its integrity. Compared with metallic materials, carbon epoxy composites show a number of advantages. Within this framework, ultrasonic tests are effective to identify the presence of defects. In this paper a Finite Element Method approach is proposed for evaluating the most effective incidence angle of an ultrasonic probe with regard to defects identification. According to our goal, the analysis has been carried out considering a single-line plane emitting source varying the probe angle of inclination. The proposed model looks promising to specially emphasize the presence of delaminations as well as massive breaking in a specimen of multilayer carbon fibre epoxy. Subsequently, simulation parameters and results have been exploited and compared, respectively, for a preliminary experimental in-lab campaign of measurements with encouraging results.

An Optimized Support Vector Machine Based Approach for Non-Destructive Bumps Characterization in Metallic Plates

Within the framework of non-destructive testing techniques, it is very important to quickly and cheaply recognize flaws into the inspected materials. Moreover, another requirement is to carry out the inspection in an automatic way, with a total departure from the inspector’s experience, starting from the experimental measurements. In this case, a further problem is represented by the fact that many open problems within the electromagnetic diagnostic are inverse ill-posed problems. This paper just studies a method for the analysis of metallic plates, with the aim of bumps detection and characterization starting from electromagnetic measurements. The ill-posedness of the inverse problem has been overcame by using an optimized heuristic method, i.e., the so called support vector regression machines.

Evaluation of Structural Integrity of Metal Plates by Fuzzy Similarities of Eddy Currents Representation

In this paper, we present a practical application of the methodologies introduced by Jaime Gil-Aluja and Lotfi Zadeh in Civil and Electrical Engineering. Metallic plates bi-axially loaded deform producing dangerous mechanical stresses that are not visually appreciable. Being the representation construction of such stress conditions by 2D images extremely complex, in this work, we propose to generate suitable Eddy Currents (ECs) images to translate the information content of mechanical stresses into representative electric signals easier to image. By grouping the produced images in different classes related to different bi-axial loads and in a single class all the images referring to plates in absence of loads, the evaluation of the integrity of a plate is transformed into a problem of classification/decision. This further step is carried out by means of the measure of Fuzzy Similarities (Ss) between the 2D EC signal at hand and the prototypical classes. The achieved performance are comparable to more established approaches that are commonly plagued by a higher computational load. The proposed methodology is also shown to be able to manage uncertainty in an application of relevant industrial interest.

A complex network-based approach to detecting and characterizing ictal states in patients with Childhood Absence Epilepsy

This paper proposes a network analysis-based approach to detecting and characterizing ictal states in patients with Childhood Absence Epilepsy. Our approach defines and uses some suitable data structures, consisting of ad-hoc complex networks and subnetworks, and a new network analysis-based parameter, called connection coefficient. The examination of the values of this parameter on the adopted data structures allows our approach to reach its objectives. Obtained results are extremely encouraging and stimulate the extension of our approach in several directions.

Exploitation of Fuzzy Information for Tokamak Plasma Shape Recognition

A fuzzy inference model (FIM) for plasma shape recognition applications is presented. The model is directly extracted from a data set of examples of the problem without using any learning procedure. The most relevant advantages of the FIM are: 1) the solution of the problem can be expressed in terms of very simple as well as explainable rules, and 2) a very limited number of inputs is required to obtain a sufficient estimation accuracy. The first objective overcomes one of the most limitations of neural network models. The second one has a strong impact on the throughput time in real time applications. The resulting model can be tuned by varying the parameters of the membership functions (centres and variances of the gaussian functions) in order to best fit the data set distribution. The qualitative analysis of the data set may also capture relevant insight on some difficult aspect of the problem, like its basic ill-posedness and the detection of category transition. The results presented in this paper regards a benchmark database of simulated plasma equilibria in the ASDEX-Upgrade machine. The main conclusion is that a FIM is an efficient tool for real time analysis of magnetic data in tokamak reactors.