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Artificial Neural Network Modelling for Prediction of SNR Effected by Probe Properties on Ultrasonic Inspection of Austenitic Stainless Steel Weldments

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dc.contributor.authorKURTULMUŞ, MEMDUH
dc.contributor.authorsKurtulmus, Memduh
dc.date.accessioned2022-03-14T08:34:00Z
dc.date.available2022-03-14T08:34:00Z
dc.date.issued2018-05-28
dc.description.abstractMany austenitic stainless steel components are used in the construction of nuclear power plants. These components are joined by different welding processes, and radiation damages occur in the welds during the service life of the plant. The plants are inspected periodically with ultrasonic test methods. Many ultrasonic inspection problems arise due to the weld metal microstructure of austenitic stainless steel weldments. The present research was conducted in order to describe the affects of probe angle and probe frequency of both transversal and longitudinal wave probes on detecting the defects of austenitic stainless steel weldments. Feed forward back propagation artificial neural network (ANN) models have been developed for predicting signal to noise ratio (SNR) of transversal and longitudinal wave probes. Input variables that affect SNR output in these models are welding angle, probe angle, probe frequency and sound path. Of the experimental data, 80% is used for a training dataset and 20% is used for a testing dataset with 10 neurons in hidden layers in developed ANN models. Mean absolute error (MAE) and mean absolute percentage error (MAPE) types are calculated as 0.0656 and 16.28%, respectively, to predict performance of ANN models in a transversal wave probe. In addition, MAE and MAPE are calculated as 0.0478 and 18.01%, respectively, for performance in a longitudinal wave probe.
dc.identifier.doi10.1515/chem-2018-0056
dc.identifier.issn2391-5420
dc.identifier.urihttps://hdl.handle.net/11424/241981
dc.identifier.wosWOS:000433918200010
dc.language.isoeng
dc.publisherDE GRUYTER POLAND SP ZOO
dc.relation.ispartofOPEN CHEMISTRY
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectAustenitic stainless steel welding
dc.subjectUltrasonic testing
dc.subjectSignal-to-noise ratio
dc.subjectArtificial Neural Networks
dc.subjectWELDS
dc.titleArtificial Neural Network Modelling for Prediction of SNR Effected by Probe Properties on Ultrasonic Inspection of Austenitic Stainless Steel Weldments
dc.typearticle
dspace.entity.typePublication
local.avesis.ida8273ab3-c93b-4004-9d72-ee2c427803a1
local.import.packageSS16
local.indexed.atWOS
local.indexed.atSCOPUS
local.journal.numberofpages5
local.journal.quartileQ3
oaire.citation.endPage515
oaire.citation.issue1
oaire.citation.startPage511
oaire.citation.titleOPEN CHEMISTRY
oaire.citation.volume16
relation.isAuthorOfPublicationc6636ff2-3069-4432-8ed0-4735afeeffc0
relation.isAuthorOfPublication.latestForDiscoveryc6636ff2-3069-4432-8ed0-4735afeeffc0

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