Parametric Analysis to Quantify Process Input Influence on the Printed Densities of Binder Jetted Alumina Ceramics

Abstract

Abstract Binder jetting, a commercial additive manufacturing process that selectively deposits a liquid binder onto a powder bed, can become a viable method to additively manufacture ceramics. However, the effects of process parameters/inputs on printing outputs (e.g. part density and geometric resolution) have not been investigated and no methodical approach exists for the process development of new materials. In this work, a parametric study consisting of 18 experiments with unique process input combinations explores the influence of seven process inputs on the relative densities of as-printed (green) alumina (Al2O3) parts. Sensitivity analyses compare the influence of each input on green densities. Multivariable linear and Gaussian process regressions provide models for predicting green densities as a function of binder jetting process inputs. The parametric study reveals that two process inputs, namely recoat speed and oscillator speed, significantly influence green densities. The multivariable linear and Gaussian process regression models indicate that the green densities of alumina builds can be increased by decreasing the recoat speed and increasing the oscillator speed. The Gaussian process regression model further suggests that the green densities have nonlinear dependence on the rest of the process parameters. Separate prints were performed at process input combinations different than those of the parametric study to validate the green density models. The models produced can assist operators in selecting process inputs that will result in a desired green density, allowing for the control of porosity in printed parts with a high degree of accuracy. The methodology reported in this study can be leveraged for other powder systems and machines to predict and control the porosity of binder jetted parts for applications such as filters, bearings, electronics, and medical implants.