AI-driven models for prediction of ceramic tiles' properties and detection of the influences: Behavior during shaping and drying.

The shaping and drying of ceramics are a critical yet complex process that directly influences ceramic materials' final properties and performance. Predicting key parameters such as the coefficient of plasticity, mass loss during drying in the air at the critical point, and shaping moisture is essential for optimizing these processes. This study analyzes the dataset of the clays of various chemical compositions to predict and reveal the most important influences on the shaping and drying parameters in producing ceramic tiles. The data are then employed to develop and compare four advanced machine learning models. The models were evaluated using the most important performance metrics such as the coefficient of determination (²), mean absolute percentage error (MAPE), mean absolute error (MAE), and root mean squared error (RMSE). Extreme Gradient Boosting (Gradient Boosting) emerged as the most reliable model, with 0.9871 R², 0.2672 RMSE, 0.2086 MAE, and 1.61% MAPE. Support vector regression and artificial neural networks also delivered strong performances, while random forest, though competitive, was slightly less accurate. Furthermore, model interpretation methods in machine learning analysis provided valuable validation of the predictive capabilities of the models and the influence of key input features. The advanced machine learning techniques in optimizing ceramic shaping processes offer a robust predictive toolkit for enhancing efficiency, reliability, and sustainability in ceramic materials engineering. It is seen that the AlO levels up to 23% had little effect on plasticity and drying susceptibility, with significant changes occurring above 28%. The critical FeO content is found between 1.5% and 1.7%, and SiO of up to about 62%. The findings of this study offer valuable decision-support tools for ceramic manufacturers, raw material suppliers, and process engineers, enabling more informed material selection, reduced waste, and improved product consistency across the industry.