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

Content Provider	IEEE Xplore Digital Library
Author	Xiaojuan Wang Shejuan Xie Zhenmao Chen
Copyright Year	1965
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.
Sponsorship	IEEE Magnetics Society
Starting Page	125
Ending Page	128
Page Count	4
File Size	530741
File Format	PDF
ISSN	00189464
Volume Number	50
Issue Number	2
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-01-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Artificial neural networks Cavity resonators Shape Inverse problems Databases Materials Vectors nondestructive testing FEM inversion problem
Content Type	Text
Resource Type	Article
Subject	Electronic, Optical and Magnetic Materials Electrical and Electronic Engineering

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