New Approaches to Ultrasonic Flaw Classification Using Signal Processing, Modeling, and Artificial Intelligence Concepts

dc.contributor.author Schmerr, Lester
dc.date 2018-02-14T07:43:21.000
dc.date.accessioned 2020-06-30T06:31:24Z
dc.date.available 2020-06-30T06:31:24Z
dc.date.copyright Wed Jan 01 00:00:00 UTC 1986
dc.date.issued 1986
dc.description.abstract <p>There are a number of modern approaches that can be used to characterize flaws in materials. For example, one method, which has been described recently by Wormley and Thompson [1], uses a model-based approach to obtain the “best fit” size and orientation parameters based on a simple equivalent shape such as ellipsoid. Before such sizing estimates can be activated, however, it is first necessary to determine if the unknown flaw being examined is a volumetric or crack-like flaw, since the sizing algorithm will be different for each case. This classification problem, although it is conceptually simpler than the more complete problem of flaw characterization, is, nevertheless, a difficult challenge because of the large number of parameters that can influence the resulting signals. A summary of our recent work on the flaw classification problem is given below. As will be shown, we have chosen to use a combination of signal processing, modeling and artificial intelligence tools to try to pare down the complexity of the ultrasonic responses and isolate those features that are dependent only on flaw-type.</p>
dc.format.mimetype application/pdf
dc.identifier archive/lib.dr.iastate.edu/qnde/1986/allcontent/79/
dc.identifier.articleid 3428
dc.identifier.contextkey 5809711
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath qnde/1986/allcontent/79
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/58846
dc.language.iso en
dc.relation.ispartofseries Review of Progress in Quantitative Nondestructive Evaluation
dc.source.bitstream archive/lib.dr.iastate.edu/qnde/1986/allcontent/79/1986_Schmerr_NewApproaches.pdf|||Sat Jan 15 01:55:31 UTC 2022
dc.source.uri 10.1007/978-1-4615-7763-8_79
dc.subject.keywords Engineering Mechanics
dc.title New Approaches to Ultrasonic Flaw Classification Using Signal Processing, Modeling, and Artificial Intelligence Concepts
dc.type event
dc.type.genre article
dspace.entity.type Publication
relation.isSeriesOfPublication 289a28b5-887e-4ddb-8c51-a88d07ebc3f3
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