| Prediction of Wear Properties of CaO and MgO Doped
Stabilized Zirconia Ceramics with Artificial Neural Networks |
Ahmet Gürkan Yüksek,a Tahsin Boyrazb, and Ahmet Akkusc
Pages : 188-198
DOI : 10.1080/0371750X.2024.2401783 |
| Abstract |
| In this research, the production and wear behavior of CaO and MgO doped stabilized
zirconia ceramics prepared by powder metallurgy method were investigated and
artificial neural network (ANN) models were established and predicted with the data
produced as a result of real experiments. CaO/MgO doped stabilized zirconia ceramics
were produced using a combination of ball milling, cold pressing - cold isostatic
pressing and sintering methods. CaO and MgO were mixed with zirconia in different
amounts (0-8 mol%). These mixtures were prepared by mechanical alloying. Green
compacts were sintered at 1600oC. The wear experimental results produced from the
trials were converted into data suitable for modelling with ANN. Wear load, wear time
of the load, MgO and CaO were given as input to the ANN model. The amount of wear
according to the pressing method was set as the output variable value of the ANN. An
ANN approach model was established to predict the wear behavior characteristics of
zirconia ceramic composites. In order to emphasize the success of the model, the
test data set was presented to the ANN model and the results produced were compared
with the results produced experimentally, and as a result of these tests, high R2 values
of 0.99409 for 65N and 0.97512 for 80N were produced.
[Keywords: Ceramics, Zirconia, Wear, Artificial neural networks] |
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