Mechanical Engineering / Makina Mühendisliği

Permanent URI for this collectionhttps://hdl.handle.net/11147/4129

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Subject: search.filters.subject.Artificial neural networksWoS Q: search.filters.wosQuartile.Q3

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  • Article
    Doe and Ann Models for Powder Mixture Packing
    (American Ceramic Society, 2007) Akkurt, Sedat; Romagnoli, Marcello; Sütçü, Mücahit
    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.
  • Article
    Citation - WoS: 121
    Citation - Scopus: 136
    Quantification of Caco3-Caso3·0.5h 2o-Caso4·2h2o Mixtures by Ftir Analysis and Its Ann Model
    (Elsevier Ltd., 2004) Böke, Hasan; Akkurt, Sedat; Özdemir, Serhan; Göktürk, E. Hale; Caner Saltık, Emine N.
    A new quantitative analysis method for mixtures of calcium carbonate (CaCO3), calcium sulphite hemihydrate (CaSO 3·1/2H2O) and gypsum (CaSO 4·2H2O) by FTIR spectroscopy is developed. The method involves the FTIR analysis of powder mixtures of several compositions on KBr disc specimens. Intensities of the resulting absorbance peaks for CaCO 3, CaSO3·1/2H2O and CaSO 4·2H2O at 1453, 980, 1146 cm-1 were used as input data for an artificial neural network (ANN) model, the output being the weight percent compositions of the mixtures. The training and testing data were randomly separated from the complete original data set. Testing of the model was done with successfully low-average error levels. The utility of the model is in the potential ability to use FTIR spectrum to predict the proportions of the three substances in unknown mixtures.