首页|Prediction of viscoelastic and printability properties on binder-free TiO_2-based ceramic pastes by DIW through a machine learning approach
Prediction of viscoelastic and printability properties on binder-free TiO_2-based ceramic pastes by DIW through a machine learning approach
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NETL
NSTL
Elsevier
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 1×10~3 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.
Machine learningDeep neural networksDIW 3D printingCeramicsTiO_2
Luis Antonio Pulido-Victoria、Antonio Flores-Tlacuahuac、Alexander Panales-Perez、Tania E. Lara-Ceniceros、Manuel Alejandro Avila-Lopez、Jose Bonilla-Cruz
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Tecndlogico de Monterrey, Escuela de Ingenieria y Ciencias, Ave. Eugenio Garza Sada 2501, Monterrey, N.L., 64849, Mexico
Nano and Micro Additive Manufacturing of Polymers and Composite Materials Laboratory '3D LAB', Centro de Investigacion en Materiales Avanzados S.C. (CIMAV-Subsede Monterrey), Ave. Alianza Norte 202, Monterrey-Aeropuerto Km 10, PIIT, Apodaca, Nuevo Leon-Mexico, C.P. 66628, Mexico
NETL
NSTL
Elsevier
