Prediction of viscoelastic and printability properties on binder-free TiO2-based ceramic pastes by DIW through a machine learning approach

Ceramic 3D printing has become an increasingly popular manufacturing technique due to its potential to create complex geometries with high precision. However, predicting the printability of ceramic pastes remains a challenge, as it depends on various rheological properties. In this study, we propose a feed-forward deep neural network model that predicts the printability of ceramic pastes based on two suggested criteria, a shear-thinning ability and a gel point greater than $1\times 10^3$ Pa. The model is trained on a dataset built from rheological and viscoelastic characterizations of pastes, and validated on a separate test set. Our results show that the proposed learning model achieves small relative error in predicting the gel point of the ceramic pastes, with a mean value of 8.99181 and a standard deviation of 1.812864. This model has the potential to improve the efficiency and quality of ceramic 3D printing by enabling rapid and accurate predictions of paste printability.