Crack Parameter Characterization by a Neural Network Takadoya, M. Achenbach, J. Guo, G. C. Kitahara, M. 2018-02-14T07:08:38.000 2020-06-30T06:44:16Z 2020-06-30T06:44:16Z Mon Jan 01 00:00:00 UTC 1996 1996
dc.description.abstract <p>A neural network with binary outputs is presented to determine the angle and the depth of a surface-breaking crack from ultrasonic backscattering data. The estimation procedure is divided into two steps: (1) The angle of the crack is estimated in the range from 10 to 70 degrees with a precision of 5 degrees. To improve the accuracy of estimation, information on the integral of the backscattered signal is utilized. (2) When the angle of the crack has been estimated, the depth of the crack is determined with a precision of 0.5mm in the range from 2.0mm to 4.0mm. This determination is achieved by employing sets of neural networks corresponding to various angles of the crack.</p>
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dc.identifier archive/
dc.identifier.articleid 3148
dc.identifier.contextkey 5807211
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath qnde/1996/allcontent/104
dc.language.iso en
dc.relation.ispartofseries Review of Progress in Quantitative Nondestructive Evaluation
dc.source.bitstream archive/|||Fri Jan 14 18:20:00 UTC 2022
dc.source.uri 10.1007/978-1-4613-0383-1_104
dc.subject.disciplines Signal Processing
dc.title Crack Parameter Characterization by a Neural Network
dc.type event
dc.type.genre article
dspace.entity.type Publication
relation.isSeriesOfPublication 289a28b5-887e-4ddb-8c51-a88d07ebc3f3
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