Prediction of the Fracture Voltage of TiO2-Doped ZnO-Bi2O3-MnO-CoO Ceramics Produced by the Chemical Precipitation Method with Using Artificial Neural Networks |
| S. Arslankayaa, N.K. Kayab, H.Ö Toplanb
aSakarya University, Engineering Faculty, Industrial Engineering Department, Esentepe Campus, Serdivan, Sakarya, Turkey bSakarya University, Engineering Faculty, Metallurgical and Materials Engineering Department, Esentepe Campus, Serdivan, Sakarya, Turkey |
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| In this study, TiO2 (2.5 and 5.0 wt%) doped ZnO-Bi2O3-MnO-CoO were produced by chemical precipitation method. In the ceramics produced, the effect of TiO2 addition, sintering temperature, and time on breakdown voltage was experimentally measured and a mathematical model was developed according to these results. Based on the developed mathematical model and experimental results, an artificial neural network model was developed to determine the effect of TiO2 on the breakdown voltage depending on the sintering temperature and time and was estimated by the breakdown voltage values. The results of the mathematical model and the artificial neural networks were statistically compared with the t test. |
DOI:10.12693/APhysPolA.135.713 topics: breakdown voltage, mathematical model, artificial intelligent, artificial neural networks |