ANN model for prediction of powder packing

SÜTÇÜ M., Akkurt S.

Journal of the European Ceramic Society, cilt.27, sa.2-3, ss.641-644, 2007 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 27 Sayı: 2-3
  • Basım Tarihi: 2007
  • Doi Numarası: 10.1016/j.jeurceramsoc.2006.04.044
  • Dergi Adı: Journal of the European Ceramic Society
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.641-644
  • Anahtar Kelimeler: pressing, porosity, Al2O3, artificial neural network (ANN)
  • Manisa Celal Bayar Üniversitesi Adresli: Hayır

Özet

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%. © 2006 Elsevier Ltd. All rights reserved.