Application of ANN in the prediction of the pore concentration of aluminum metal foams manufactured by powder metallurgy methods

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dc.contributor.authorOzan, Sermin
dc.contributor.authorTaskin, Mustafa
dc.contributor.authorKolukisa, Sedat
dc.contributor.authorOzerdem, Mehmet Sirac
dc.date.accessioned2024-04-24T15:59:46Z
dc.date.available2024-04-24T15:59:46Z
dc.date.issued2008
dc.departmentDicle Üniversitesien_US
dc.description.abstractIn this work, the effect of fabrication parameters on the pore concentration of aluminum metal foam, manufactured by the powder metallurgy process, has been studied. The artificial neural network (ANN) technique has been used to predict pore concentration as a function of some key fabrication parameters. Aluminum metal foam specimens were fabricated from a mixture of aluminum powders (mean particle size 60 mu m) and NaCl at 10, 20, 30, 40(wt)% content under a pressure of 200, 250, and 300 MPa. All specimens were then sintered at 630 degrees C for 2.5 h in argon atmosphere. For pore formation (foaming), sintered specimens were immersed into 70 degrees C hot running water. Finally, the pore concentration of specimens was recorded to analyze the effect of fabrication parameters (namely, NaCl ratio, NaCl particle size, and compacting pressure) on the foaming behavior of compacted specimens. It has been recorded that the above-mentioned fabrication parameters are effective on pore concentration profile while pore diameters remain unchanged. In the ANN training module, NaCl content (wt)%, NaCl particle size (mu m), and compacting pressure (MPA) were employed as inputs, while pore concentration % (volume) of compacts related to fabrication parameters was employed as output. The ANN program was successfully used to predict the pore concentration % (volume) of compacts related to fabrication parameters.en_US
dc.description.sponsorshipDicle University Research Committee [DUAPAK 03-MF-86]; TUBITAK (The Scientific and Technological Research Council of Turkey)en_US
dc.description.sponsorshipThe authors are grateful to the Dicle University Research Committee since this work is suggested and supported through grant DUAPAK 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.doi10.1007/s00170-007-1218-2
dc.identifier.endpage256en_US
dc.identifier.issn0268-3768
dc.identifier.issue3-4en_US
dc.identifier.scopus2-s2.0-52649177187
dc.identifier.scopusqualityQ1
dc.identifier.startpage251en_US
dc.identifier.urihttps://doi.org/10.1007/s00170-007-1218-2
dc.identifier.urihttps://hdl.handle.net/11468/14250
dc.identifier.volume39en_US
dc.identifier.wosWOS:000259527900005
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoenen_US
dc.publisherSpringer London Ltden_US
dc.relation.ispartofInternational Journal of Advanced Manufacturing Technology
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectAluminum Metal Foamen_US
dc.subjectPore Concentrationen_US
dc.subjectFabrication Parametersen_US
dc.subjectNeural Networken_US
dc.titleApplication of ANN in the prediction of the pore concentration of aluminum metal foams manufactured by powder metallurgy methodsen_US
dc.titleApplication of ANN in the prediction of the pore concentration of aluminum metal foams manufactured by powder metallurgy methods
dc.typeArticleen_US

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