Pattern Recognition Analysis of Acoustic Emission Data from 7075-T651 Aluminum Simulated Joint Specimens

dc.contributor.author Melton, R.
dc.contributor.author Doctor, P.
dc.contributor.author Daly, D.
dc.date 2018-02-14T02:21:19.000
dc.date.accessioned 2020-06-30T06:29:21Z
dc.date.available 2020-06-30T06:29:21Z
dc.date.copyright Sat Jan 01 00:00:00 UTC 1983
dc.date.issued 1983
dc.description.abstract <p>The objective of the work described in this paper is to develop signal analysis techniques that can automatically discriminate be-tween non-critical acoustic emission (AE) from crack growth and acoustic noise signals, such as fretting, of fasteners. The ultimate application of this work is for in-flight AE monitoring of critical aircraft structures.</p> <p>Fatigue crack growth experiments were performed with center notched plate specimens and simulated joint specimens of 7075-T651 aluminum. The experimental conditions were controlled such that acoustic signals were obtained from crack growth, crack interface rubbing, and from fastener fretting.</p> <p>This paper reports the results of pattern recognition analysis of the signals using autocorrelation lags and statistical measures of the signals and their power spectra as features. The goal of the pattern recognition analysis was to isolate crack growth AE signals from the other acoustic data. The results indicate that autocorrelation lags are the most important features for discriminating these signals.</p>
dc.format.mimetype application/pdf
dc.identifier archive/lib.dr.iastate.edu/qnde/1983/allcontent/29/
dc.identifier.articleid 1028
dc.identifier.contextkey 5761608
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath qnde/1983/allcontent/29
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/58555
dc.language.iso en
dc.relation.ispartofseries Review of Progress in Quantitative Nondestructive Evaluation
dc.source.bitstream archive/lib.dr.iastate.edu/qnde/1983/allcontent/29/1983_Melton_PatternRecognition.pdf|||Fri Jan 14 23:12:43 UTC 2022
dc.source.uri 10.1007/978-1-4613-3706-5_29
dc.subject.disciplines Acoustics, Dynamics, and Controls
dc.title Pattern Recognition Analysis of Acoustic Emission Data from 7075-T651 Aluminum Simulated Joint Specimens
dc.type event
dc.type.genre article
dspace.entity.type Publication
relation.isSeriesOfPublication 289a28b5-887e-4ddb-8c51-a88d07ebc3f3
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