Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

Permanent URI for this collectionhttps://hdl.handle.net/11147/7148

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Author: search.filters.author.Sütçü, MücahitSubject: search.filters.subject.Artificial neural networks

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  • Article
    Citation - WoS: 11
    Citation - Scopus: 18
    Ann Model for Prediction of Powder Packing
    (Elsevier Ltd., 2007) Sütçü, Mücahit; Akkurt, Sedat; Akkurt, Sedat; 03.09. Department of Materials Science and Engineering; 03. Faculty of Engineering; 01. Izmir Institute of Technology
    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%.
  • Article
    Doe and Ann Models for Powder Mixture Packing
    (American Ceramic Society, 2007) Akkurt, Sedat; Romagnoli, Marcello; Akkurt, Sedat; 03.09. Department of Materials Science and Engineering; 03. Faculty of Engineering; 01. Izmir Institute of Technology
    Design of experiments (DOE) and artificial neural network (ANN) techniques were used to study packing of fused alumina powders composed of three different sizes of particles. The first is the mixture design technique that produces a polynomial model of the powder-packing system. While, the ANN technique is extensively used to model complex systems in many fields. The methodological approach used is mixture design, which can be used to study the influences of two or more additives. It is a structured and organized method for determining the relationship between the components and the output of that process. The mixture design approach permits optimization of size distribution to obtain a target value of porosity. Sensitivity analysis involves the use of the developed ANN model to predict outputs (porosity) at varying levels of the input factor effects.