Modeling and simulation of the flash sintering: An approach to study the influence of process attributes on the flash phenomenon and microstructure

Abstract

Field-assisted sintering still has gaps in explaining and modeling the flash phenomenon, as well as in enabling control over the sintering process to achieve desirable densification and microstructure control in ceramic materials.This study investigates methods for constructing computational models based on machine learning for predicting the onset temperature of the flash event (Tonset) and bulk density (BD) from a combination of various process attributes: powder composition, green sample geometry, electric field configuration, and heating. Samples of 3 mol% and 8 mol% yttria-stabilized zirconia were shaped into cylindrical geometry and were sintered using flash sintering and multi-step flash sintering processes, employing the following variations: current density; DC and AC electric field; and holding time. The experimental data were used for constructing and evaluating prediction models for Tonset and BD using k-fold cross-validation and the following algorithms: Random Forest, Support Vector Machine, Artificial Neural Network, K-Nearest Neighbors, and Multivariate Linear Regression Analysis. The model performance was assessed by comparing the measured values with the respective predicted values based on the Pearson correlation coefficient (r), mean absolute error (MAE), and root mean square error (RMSE). The models based on the Artificial Neural Network algorithm showed the best performance for predicting Tonset (r = 0.98; MAE = 11.88; RMSE = 14.88) and BD (r = 0.91; MAE = 1.86; RMSE = 2.30). The best models were used in conjunction with the Wrapper method for selecting attributes that significantly impact the sintering process, allowing the identification of 6 main attributes that impact Tonset and 9 attributes that impact BD in the flash-sintered samples.