of the modern airplane), one of the most important aspects regards defects detection. 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 employs a rotating magnetic field is presented. This approach has been carried out by a Finite Element Model. In this context, the requirement of a perfect state for used welded joints is unavoidable in order to assure both reliability and safety in industrial and civil application. Therefore, a real-time approach able to recognize and estimate defect dimensions starting from the simulated data could be a very 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. In the last decades, a useful and very performing way to regularize ill-posed inverse electromagnetic problems is based on the use of a Neural Network approach, the so called “learning by sample techniques”. Obtained results assure good performances of the implemented method, with very interesting application.
|Titolo:||On the Use of Ferraris Effect and Artificial Intelligence for Characterizing Voids in Aeronautical Weldings|
|Data di pubblicazione:||2008|
|Appare nelle tipologie:||1.1 Articolo in rivista|