Mechanical Engineering / Makina Mühendisliği
Permanent URI for this collectionhttps://hdl.handle.net/11147/4129
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Article Citation - WoS: 54Citation - Scopus: 58Sintering and Microstructural Investigation of Gamma–alpha Alumina Powders(Elsevier Ltd., 2014) Yalamaç, Emre; Trapani, Antonio; Akkurt, SedatSintering behaviors of commercially available alumina powders were investigated using constant-heating rate dilatometric experiments. Each powder had different proportion of alpha/gamma alumina. Densification behaviors of powders were studied up to 1600 °C with three different heating rates of 1, 3.3 and 6.6 °C/min. Compacts of different gamma content alumina powders exhibited systematic anomalous second peaks in the densification rate curves at certain heating rates and temperatures. At 3.3 °C/min heating rate experiments, densification curves of 10% gamma phase alumina powder compacts reached a plateau after 1450 °C, and did not increase any further at higher temperatures. This phenomenon was double checked to understand powder behavior during sintering. 10% gamma phase alumina powder compacts showed the highest density for each heating rate. It reached 94% theoretical density with 1 °C/min heating rate. But 20% gamma phase alumina powder compacts had the finest grain size of about 1.40 ?m. Final density and porosity of compacts were also tested by image analysis and the results were coherent with Archimedes results. © 2014 Karabuk UniversityArticle Citation - WoS: 11Citation - Scopus: 18Ann Model for Prediction of Powder Packing(Elsevier Ltd., 2007) Sütçü, Mücahit; Akkurt, SedatA 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%.