The use of artificial neural network for prediction of grain size of 17-4 pH stainless steel powders

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dc.contributor.authorFindik, Tayfun
dc.contributor.authorTasdemir, Sakir
dc.contributor.authorSahin, Ismail
dc.date.accessioned2020-03-26T18:05:25Z
dc.date.available2020-03-26T18:05:25Z
dc.date.issued2010
dc.departmentSelçuk Üniversitesien_US
dc.description.abstractThis study is aimed to deals with artificial neural network (ANN) approach for prediction grain size (GS) of 17 - 4 pH stainless steel powders. Experimental data which were obtained from experimental studies in a laboratory environment have been used for this modeling. Using some of the experimental data for training and testing an ANN for GS was developed. In these systems, output parameters GS has been determined using input parameters including environment, time, speed, ball diameter, ball ratio, and material. When experimental data and results obtained from ANN were compared by regression analysis in Matlab, it was determined that both groups of data are consistent. The correlation coefficient between estimated GS values and experimental data obtained are 0.99 for traing and 0.98 for testing respectively. The correlation coefficient is closely to 1. This coefficient shows that there is a strong relationship between these data. Also, the accuracy rate was 98.97% for GS. As a result, it has been shown that designed ANN can be used reliably in powder metallurgic industry and engineering.en_US
dc.identifier.endpage1283en_US
dc.identifier.issn1992-2248en_US
dc.identifier.issue11en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage1274en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12395/25419
dc.identifier.volume5en_US
dc.identifier.wosWOS:000279559800008en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherACADEMIC JOURNALSen_US
dc.relation.ispartofSCIENTIFIC RESEARCH AND ESSAYSen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectArtificial neural networken_US
dc.subjectmechanical millingen_US
dc.subjectgarin sizeen_US
dc.titleThe use of artificial neural network for prediction of grain size of 17-4 pH stainless steel powdersen_US
dc.typeArticleen_US

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