Diego Pellicanò

Elman neural networks for characterizing voids in welded strips: a study

Within the framework of aging materials inspection, one of the most important aspects regarding defects detection in metal welded strips. In this context, it is important to plan a method able to distinguish the presence or absence of defects within welds as well as a robust procedure able to characterize the defect itself. In this paper, an innovative solution that exploits a rotating magnetic field is presented. This approach has been carried out by a finite element model. Within this framework, it is necessary to consider techniques able to offer advantages in terms of sensibility of analysis, strong reliability, speed of carrying out, low costs: its implementation can be a useful support for inspectors. To this aim, it is necessary to solve inverse problems which are mostly ill-posed; in this case, the main problems consist on both the accurate formulation of the direct problem and the correct regularization of the inverse electromagnetic problem. We propose a heuristic inversion, regularizing the problem by the use of an Elman network. Experimental results are obtained using a database created through numerical modeling, confirming the effectiveness of the proposed methodology.

Wavelet Coherence and Fuzzy Subtractive Clustering for Defect Classification in Aeronautic CFRP

Despite their high specific stiffness and strength, carbon fiber reinforced polymers, stacked at different fiber orientations, are susceptible to interlaminar damages. They may occur in the form of micro-cracks and voids, and leads to a loss of performance. Within this framework, ultrasonic tests can be exploited in order to detect and classify the kind of defect. The main object of this work is to develop the evolution of a previous heuristic approach, based on the use of Support Vector Machines, proposed in order to recognize and classify the defect starting from the measured ultrasonic echoes. In this context, a real-time approach could be exploited to solve real industrial problems with enough accuracy and realistic computational efforts. Particularly, we discuss the cross wavelet transform and wavelet coherence for examining relationships in time-frequency domains between. For our aim, a software package has been developed, allowing users to perform the cross wavelet transform, the wavelet coherence and the Fuzzy Inference System. Since the ill-posedness of the inverse problem, Fuzzy Inference has been used to regularize the system, implementing a data-independent classifier. Obtained results assure good performances of the implemented classifier, with very interesting applications.

Rotating Electromagnetic Field for Crack Detection in Railway Tracks

The main problem about a railway analysis is detection of cracks in the structure. If these deflciencies are not controlled at early stages they might cause huge economical problems afiecting the rail network (unexpected requisition of spare parts, handling of incident and/or accidents). Within this framework, the early and continuous use of Non Destructive Tests can be useful. In this context, Eddy Current Testing is increasing in importance and popularity. Particularly, in this paper we exploit the measure of normal component, with respect to the scanned surface, of magnetic fleld. Whilst the scientiflc literature proposes a lot of solutions for detecting sub-superflcial defects, an open problem is related to the geometrical complexity of the structure and the relevant di-culty of crack detection. In this paper, we propose a Finite Element Method based approach for modelling a fast and accurate evaluation of the defect in railways tracks. The modelled system is strongly versatile and the choice of electrical parameters afiect the design of new probes for this kind of inspection. In particular, we propose a solution exploiting a rotating electromagnetic fleld with very encouraging results: The proposed model is able to recognize deep and surface cracks even if their orientations is vertical to the longitudinal direction of the sensor.

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.

Modelling and Validating Ferrite-core Probes for GMR-eddy Current Testing in Metallic Plates

Non Destructive Testing techniques are more and more exploited in order to quickly and cheaply recognize ∞aws into the inspected materials. Within this framework, modelling is a powerful tool for inspection improvements. It helps probe-coil designers to optimize sensors for each examination requirement, providing better understanding of the involved physics, supporting operator training and increasing defect analysis reliability. The efiect of the ferrite core is analyzed in order to optimize the design of probe-coils and study various conflgurations of inspection. Particularly, Finite Element based analyzes will be carried out into this path. Direct problem will be assessed, and direct model will be formulated, dependent by difierent parameters, e.g., coil shape, working frequencies and so forth. The model will be subsequently validated by in-lab experimentations. 1. INTRODUCTION Non Destructive Testing and Evaluation (NDT/E) of metallic materials ofiers an exciting and interesting challenge to both researchers and applied technologists. For this kind of materials, the major problem of interest is the detection, location, orienting and sizing of single cracks, nondestructive evaluation of homogenous materials is presently in a state of detecting a variety of damage modes. In plane words, the present state of knowledge concerning what damage mode in metallic materials is responsible for the flnal failure process is still unclear. Hence, it is still not possible to suggest to NDT/E personnel exactly what type of damage, size, orientation, etc. needs to be found in an inspection process. Hence, the challenge. The excitement for researchers in the area of NDT/E of metallic plates is in the fact that NDT/E is presently playing an important role in helping to identify damage mechanisms in homogenous materials and to characterize the role played by these damage mechanisms in the flnal failure process. A variety of NDT/E techniques has been applied extensively to the investigation and characterization of metallic materials. Generally, one flnds that a combination of complementary NDT/E techniques are appropriate, and often required, to obtain as complete information as possible on the damage state of the specimen. Eddy-current (EC) methods are commonly applied because of their relative ease of their use, and the relative amount of information that can be obtained from them. As far as metals are concerned, the EC techniques has been used for quite some time for varied applications such as the detection of cracks, porosities and inclusions; metal sorting, evaluation of plate or tubing thickness, measurement of coating thickness and the thickness of non-conducting fllms on metallic bases and so forth 1 When a coil carrying alternating current is brought near an electrically conducting material, eddy-currents are induced in the materials by electromagnetic induction. The magnitude of the induced eddy currents depends upon the magnitude and frequency of alternating current; the distance between the current-carrying coil and the material under test; the presence of defects or inhomogeneities in the material and the physical properties of the material. The induced eddy-currents modulate the impedance of the exciting coil or any secondary coil situated in the vicinity of the test material. The difierence between the original coil impedance and the modulated coil impedance (due to the presence of eddy currents) is monitored to obtain meaningful information regarding the presence of defects or changes in physical, chemical or microstructural properties. Typical testing conflgurations may consist of ferrite core coil probes, placed above a planar (or at least locally planar) conductive specimen and operating in the time-harmonic domain, at frequency depending on the problem (typically between a few (Hz) to a few (MHz)) (2). The aim of ferrite core is to focus the magnetic flelds into the specimen, in order to increase the probe sensitivity to the defect. For each application, the coil model as well as the operating frequencies are set according to the task. This work proposes an integrated approach starting from the design and implementation of a novel probe in order to optimize the sensor efiect and the drop-in suppression, the operating parameters of the frequency

Innovative Fuzzy Techniques for Characterizing Defects in Ultrasonic Nondestructive Evaluation

Classification of defects in ultrasonic nondestructive testing (NDT)/nondestructive evaluation (NDE) has a role of primary importance in all those applications in which the knowledge of the typology of defect is crucial for the manufact destination. In such a context, the necessity to have efficient investigation instruments for a correct classification analysis emerges clearly. A defect, even when invisible to the naked eye, can be revealed as the cause that reduces the similarity of a measured signal with respect to a reference. Considering the fuzziness intrinsic in the signals, the reliance on fuzzy techniques to evaluate similarity appears desirable. Two fundamental achievement of research in such field, which both derive from the fuzzy thinking and which share common traits, are computing with words (CW) and the concept of fuzzy similarity (FS) . In CW, a word is considered as a label of a fuzzy set of points clustered by similarity (granule) which lead to a particular formulation of a bank of fuzzy rules structured per classes. FS is an evaluation index of similarity among entities (for example signals) particularly useful for the formation of specific classes. Both approaches are based on computational linguistics (for example, descriptive formalism in natural language). This chapter is conceptually divided into two parts: the first one is dedicated to the development of detection and classification techniques of defectiveness for ultrasonic NDE by means of CW, while the second one, with the same purpose, proposes an approach based on FS.

Fuzzy similarities for the optimization of the operational parameters related to ultrasonic phased array probes for industrial application

The systematic use of non-destructive testing is important both for on-line quality control during the production process and the maintenance of complex equipment especially in industrial applications. In such a context, the ultrasound imaging techniques cover an important role above all in those cases in which it is requested a great penetration depth. Starting from an experimental database (affected by uncertainties due to sampling and/or noise, so of clear fuzzy nature) , the authors propose a new approach aimed at the optimization of the operational parameters of ultrasonic phased array probes. the problem of optimization is translated in an equivalent problem of classification through a fuzzy similarity approach: different images produced by different probes are compared. The quality of the proposed procedure has been assessed comparing the obtained results also with SOM- type technique in which the parameters of similarity among neurons has been considered of fuzzy nature.

Rotating Electromagnetic Field for Ndt Inspections

Reliable performance of a component or structure depends on its pre-service quality and in-service degradation under operating conditions. The importance of Non-Destructive Testing and Evaluation is ever increasing, above all in ensuring pre-service quality and monitoring in-service degradation, in order to avoid premature failure of the components/structures. There are many Non-Destructive Techniques based on various physical principles. In this work, our main objective is the characterization of anomalies such as defects, stresses and microstructural degradations in materials. Particularly, in this work we propose a Finite Element Method based approach for modelling a fast and accurate evaluation of defects in metallic materials able to easily detect defects, aside from the orientation. Within this framework, the paper proposes the application of a magnetic fleld rotating perpendicularly to the analysed specimen. We discuss a few case studies, starting from numerical simulations and flnally highlighting the importance of this approach in order to evaluate the structural integrity assessment.

Heuristic enhancement of magneto-optical images for NDE

The quality of measurements in nondestructive testing and evaluation plays a key role in assessing the reliability of different inspection techniques. Each different technique, like the magneto-optic imaging here treated, is affected by some special types of noise which are related to the specific device used for their acquisition. Therefore, the design of even more accurate image processing is often required by relevant applications, for instance, in implementing integrated solutions for flaw detection and characterization. The aim of this paper is to propose a preprocessing procedure based on independent component analysis (ICA) to ease the detection of rivets and/or flaws in the specimens under test. A comparison of the proposed approach with some other advanced image processing methodologies used for denoising magneto-optic images (MOIs) is carried out, in order to show advantages and weakness of ICA in improving the accuracy and performance of the rivets/flaw detection.

Mechanical Aspects in the Cells Detachment

Living cells possess properties that enable them to withstand the physiological environment as well as mechanical stimuli occurring within and outside the body. Any deviation from these properties will undermine the physical integrity of the cells as well as their biological functions. Thus, a quantitative study in single cell mechanics needs to be conducted. In this paper we will examine fluid flow and Neo-Hookean hyperelastic deformation. The study of these problems requires quantitative information regarding the interaction between blood flow and human cell deformation under realistic physiological conditions. For our aims, a Finite Element Method (FEM) analysis package has been exploited: the cellular level model consists of a continuum representation of the field equations and a moving boundary tracking capability to allow the cell to change its shape continuously.