An Efficient Numerical Scheme for Sizing of Cavity Defect in Metallic Foam From Signals of DC Potential Drop Method

Abstract

Quantitative nondestructive testing is important to guarantee the integrity of metallic foam (MF) structures. To predict the profile of a cavity defect in an MF material, a database-type fast forward scheme is upgraded at first by introducing a kind of multimedium element (MME) for the efficient simulation of dc potential drop (DCPD) signals of MF with defect of complicated shape. Second, a code of the hybrid strategy combining the neural network and the conjugate gradient optimization method is proposed to obtain the size and the position parameters of the defect. Both simulated and measured DCPD signals are adopted to reconstruct the bubble defects in MF. The good consistency of the true and the reconstructed results demonstrated the validity of the new scheme. In addition, it is also proved that the updated database-type fast-forward scheme is efficient for the signal simulation of MF with defect of complicated shape with the help of MME, and the hybrid inverse strategy has a better numerical performance for the defect sizing.