Within the framework of aging materials inspection, one of the most important aspects regards 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. 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”.
|Titolo:||Advanced Integration of Neural Networks for Characterizing Voids in Welded Strips|
|Data di pubblicazione:||2009|
|Appare nelle tipologie:||2.1 Contributo in volume (Capitolo o Saggio)|