Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/2291
Title: Ann Model for Prediction of Powder Packing
Authors: Sütçü, Mücahit
Akkurt, Sedat
Keywords: Alumina
Artificial neural networks
Porosity
Pressing
Publisher: Elsevier Ltd.
Source: 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
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%.
URI: http://doi.org/10.1016/j.jeurceramsoc.2006.04.044
http://hdl.handle.net/11147/2291
ISSN: 0955-2219
0955-2219
1873-619X
Appears in Collections:Mechanical Engineering / Makina Mühendisliği
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

Files in This Item:
File Description SizeFormat 
2291.pdfMakale551.16 kBAdobe PDFThumbnail
View/Open
Show full item record



CORE Recommender

SCOPUSTM   
Citations

15
checked on Dec 13, 2024

WEB OF SCIENCETM
Citations

9
checked on Nov 23, 2024

Page view(s)

540
checked on Dec 9, 2024

Download(s)

474
checked on Dec 9, 2024

Google ScholarTM

Check




Altmetric


Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.