Artificial neural network approach to predict the mechanical properties of Cu-Sn-Pb-Zn-Ni cast alloys
dc.contributor.author | Ozerdem, Mehmet Sirac | |
dc.contributor.author | Kolukisa, Sedat | |
dc.date.accessioned | 2024-04-24T16:15:26Z | |
dc.date.available | 2024-04-24T16:15:26Z | |
dc.date.issued | 2009 | |
dc.department | Dicle Üniversitesi | en_US |
dc.description.abstract | In this study, an artificial neural network approach is employed to predict the mechanical properties of Cu-Sn-Pb-Zn-Ni cast alloys. In artificial neural network (ANN), multi layer perceptron (MLP) architecture with back-propagation algorithm is utilized. In Artificial Neural Network training module, Cu-Sn-Pb-Zn-Ni (wt%) contents were employed as input while yield strength, tensile strength and elongation were employed as outputs. ANN system was trained using the prepared training set (also known as learning set). After training process, the test data were used to check system accuracy, As a result of the study neural network was found successful for the prediction of yield strength, tensile strength and elongation of Cu-Sn-Pb-Zn-Ni alloys. (C) 2008 Elsevier Ltd. All rights reserved. | en_US |
dc.description.sponsorship | Dicle University Research Committee [DUAPK 03-MF-86]; TUBITAK (The Scientific and Technological Research Council of Turkey) | en_US |
dc.description.sponsorship | The author would like to thank to Dicle University Research Committee since a part of this study is supported through Grant DUAPK 03-MF-86. Special thanks to TUBITAK (The Scientific and Technological Research Council of Turkey) for their unfailing support to the researchers. | en_US |
dc.identifier.doi | 10.1016/j.matdes.2008.05.019 | |
dc.identifier.endpage | 769 | en_US |
dc.identifier.issn | 0264-1275 | |
dc.identifier.issn | 1873-4197 | |
dc.identifier.issue | 3 | en_US |
dc.identifier.scopus | 2-s2.0-57149118632 | |
dc.identifier.scopusquality | Q1 | |
dc.identifier.startpage | 764 | en_US |
dc.identifier.uri | https://doi.org/10.1016/j.matdes.2008.05.019 | |
dc.identifier.uri | https://hdl.handle.net/11468/15800 | |
dc.identifier.volume | 30 | en_US |
dc.identifier.wos | WOS:000262869100049 | |
dc.identifier.wosquality | Q2 | |
dc.indekslendigikaynak | Web of Science | |
dc.indekslendigikaynak | Scopus | |
dc.language.iso | en | en_US |
dc.publisher | Elsevier Sci Ltd | en_US |
dc.relation.ispartof | Materials & Design | |
dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject | Artificial Neural Network | en_US |
dc.subject | Prediction Of Mechanical Properties | en_US |
dc.subject | Cu-Sn-Pb-Zn-Ni Cast Alloys | en_US |
dc.title | Artificial neural network approach to predict the mechanical properties of Cu-Sn-Pb-Zn-Ni cast alloys | en_US |
dc.title | Artificial neural network approach to predict the mechanical properties of Cu-Sn-Pb-Zn-Ni cast alloys | |
dc.type | Article | en_US |