Materials, Methods & Technologies, Volume 10, 2016

Abstract: In the development and design of new products in ceramic industry unlimited number of trials has to be carried out to obtain the appropriate format due to the deformation. These trials lead to increased costs and loss of labor in the production stage. In this study, it is aimed to develop computer software to reduce these losses in the ceramic industry. In this study, in different compositions, firing temperatures and sintering times cylindrical form samples are created. These samples, after the drying process have been scanned with the 3D scanner at the same time measured using classical methods. Later on these samples are fired at different sintering times and temperatures in ceramic firing and measured using the same methods. Thus the deformations in the base, side and mouth regions of the cylindrical samples are identified. In the light of this experimental data, we are developed the Multi-Layer Perceptron Neural Network (MLPNN) model by used MatLab Toolbox. In the MLPNN model, the firing temperature, sintering time and composition of ceramic samples have been used as the input data while the amount of deformation is used as the output data. We were used 58 and 22 of the experimental specimens for the training and the testing of MLPNN model, respectively. We founded a significant relationship between MLPNN and experimental data with X2 test (p<0.001 and for base, mouth and side