Ann Model for Prediction of Powder Packing

dc.contributor.author Sütçü, Mücahit
dc.contributor.author Akkurt, Sedat
dc.coverage.doi 10.1016/j.jeurceramsoc.2006.04.044
dc.date.accessioned 2016-10-20T09:02:59Z
dc.date.available 2016-10-20T09:02:59Z
dc.date.issued 2007
dc.description.abstract A multilayer feed forward backpropagation (MFFB) learning algorithm was used as an artificial neural network (ANN) tool to predict packing of fused alumina powder mixtures of three different sizes in green state. The data used in model construction were collected by mixing and pressing powders with average particle sizes of 350, 30 and 3 μm and with narrow particle size distributions. The data sets that were composed of green densities of cylindrical pellets were first randomly partitioned into two for training and testing of the ANN models. Based on the training data an ANN model of the packing efficiencies was created with low average error levels (3.36%). Testing of the model was also performed with successfully good average error levels of 3.39%. en_US
dc.identifier.citation Sütçü, M., and Akkurt, S. (2007). ANN model for prediction of powder packing. Journal of the European Ceramic Society, 27(2-3), 641-644. doi:10.1016/j.jeurceramsoc.2006.04.044 en_US
dc.identifier.doi 10.1016/j.jeurceramsoc.2006.04.044 en_US
dc.identifier.doi 10.1016/j.jeurceramsoc.2006.04.044
dc.identifier.issn 0955-2219
dc.identifier.issn 1873-619X
dc.identifier.scopus 2-s2.0-33750974431
dc.identifier.uri http://doi.org/10.1016/j.jeurceramsoc.2006.04.044
dc.identifier.uri https://hdl.handle.net/11147/2291
dc.language.iso en en_US
dc.publisher Elsevier Ltd. en_US
dc.relation.ispartof Journal of the European Ceramic Society en_US
dc.rights info:eu-repo/semantics/openAccess en_US
dc.subject Alumina en_US
dc.subject Artificial neural networks en_US
dc.subject Porosity en_US
dc.subject Pressing en_US
dc.title Ann Model for Prediction of Powder Packing en_US
dc.type Article en_US
dspace.entity.type Publication
gdc.author.institutional Sütçü, Mücahit
gdc.author.institutional Akkurt, Sedat
gdc.author.yokid 11535
gdc.bip.impulseclass C5
gdc.bip.influenceclass C4
gdc.bip.popularityclass C4
gdc.coar.access open access
gdc.coar.type text::journal::journal article
gdc.collaboration.industrial false
gdc.description.department İzmir Institute of Technology. Mechanical Engineering en_US
gdc.description.endpage 644 en_US
gdc.description.issue 2-3 en_US
gdc.description.publicationcategory Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı en_US
gdc.description.scopusquality Q1
gdc.description.startpage 641 en_US
gdc.description.volume 27 en_US
gdc.description.wosquality Q1
gdc.identifier.openalex W2018933748
gdc.identifier.wos WOS:000243265100037
gdc.index.type WoS
gdc.index.type Scopus
gdc.oaire.accesstype BRONZE
gdc.oaire.diamondjournal false
gdc.oaire.impulse 3.0
gdc.oaire.influence 3.992542E-9
gdc.oaire.isgreen true
gdc.oaire.keywords Pressing
gdc.oaire.keywords Artificial neural networks
gdc.oaire.keywords Alumina
gdc.oaire.keywords Porosity
gdc.oaire.popularity 4.6966173E-9
gdc.oaire.publicfunded false
gdc.oaire.sciencefields 0103 physical sciences
gdc.oaire.sciencefields 02 engineering and technology
gdc.oaire.sciencefields 0210 nano-technology
gdc.oaire.sciencefields 01 natural sciences
gdc.openalex.collaboration National
gdc.openalex.fwci 2.0410543
gdc.openalex.normalizedpercentile 0.85
gdc.opencitations.count 12
gdc.plumx.crossrefcites 6
gdc.plumx.mendeley 32
gdc.plumx.scopuscites 18
gdc.scopus.citedcount 18
gdc.wos.citedcount 11
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relation.isOrgUnitOfPublication.latestForDiscovery 9af2b05f-28ac-4023-8abe-a4dfe192da5e

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