TITLE

Identification of depth and size of subsurface defects by a multiple-voltage probe sensor: Analytical and neural network techniques

AUTHOR(S)
Makarov, Sergey; Apelian, Diran; Ludwig, Reinhold; Wang, Hengli
PUB. DATE
May 2000
SOURCE
AIP Conference Proceedings;2000, Vol. 509 Issue 1, p675
SOURCE TYPE
Academic Journal
DOC. TYPE
Article
ABSTRACT
A theoretical study is conducted to identify subsurface non-contacting defects in green-state powder metallurgy compacts by electrostatic voltage measurements along the specimen’s surface. Extensive three-dimensional numerical simulations were carried out to prove a simple analytical approach, which is based on the dipole approximation for the secondary electric field. Localized defects of different shapes and depths are considered. The simulations lead to the conclusion that the depth of a non-conducting defect can be predicted rather accurately, irrespectively of the actual flaw shape. We found that for a wide variety of shapes and different depth values, there is a linear proportionality between size and voltage distance between the two voltage peaks recorded along the surface. This appears as a direct response to the presence of the defects in the superimposed electric field. The size of the defect does not obey any simple law, especially, for slender defects. However, a rough analytical formula can be established in that case as well. In addition, some conventional neural network (NN) architectures (linear, backprop, Hopfield), including a preprocessor have been applied to the defect identification scheme for the purposes of automatic recognition of noisy data. Performance and recognition errors of different NN models are discussed and the best model is proposed for simultaneous depth recovery and size recognition of the non-conducting defects. © 2000 American Institute of Physics.
ACCESSION #
6029064

 

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