Prediction of wear properties of CaO and MgO doped stabilized zirconia ceramics produced with different pressing methods using adaptive neuro fuzzy inference systems

Abstract

AbstractThe present paper describes the fabrication and wear behaviour of CaO and MgO added stabilized zirconia (ZrO2) ceramics produced by powder metallurgy method were examined and modelling with artificial neural networks was studied using the experimental data obtained. CaO/MgO added stabilized zirconia ceramics were fabricated by using a combined method of ball milling, cold pressing ‐ cold isostatic pressing and sintering. CaO and MgO in different amounts (0–8 %mole) were mixed with zirconia. These mixtures were prepared by mechanical alloying method. The green compacts were sintered at 1600 °C. The wear experimental results obtained were converted into data suitable for modelling with artificial neural networks. Wear Load, wear time, CaO and MgO data were used as artificial neural networks input variables. The amount of wear according to the pressing method was taken as the output variables of artificial neural networks. An artificial neural networks was established for the prediction of wear properties of zirconia pressed using the adaptive neuro fuzzy inference systems (ANFIS) learning technique. As a result, a high R2 value of 0.9187 for cold pressing samples and 0,9449 for cold isostatic pressing samples was achieved based on the approach of comparing the success of the model with the test data set and the result produced.