| About this Abstract |
| Meeting |
Materials Science & Technology 2020
|
| Symposium
|
Machine Learning for Discovery of Structure-Process-Property Relations in Electronic Materials
|
| Presentation Title |
Parametric Analysis to Quantify Process Input Influence on the Printed Densities of Binder Jetted Alumina Ceramics |
| Author(s) |
Edgar Mendoza Jimenez, Jack Beuth, Baby Reeja-Jayan |
| On-Site Speaker (Planned) |
Edgar Mendoza Jimenez |
| Abstract Scope |
Binder jetting 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 for the binder jetting of ceramic powder systems. In this work, a parametric study explores the influence of seven process inputs on the relative densities of as-printed (green) alumina parts. Sensitivity analyses compare the influence of each input on green densities. Multivariable linear and Gaussian process regression provide models for predicting green densities as a function of binder jetting process inputs. The models indicate that the green densities of alumina builds can be increased by decreasing the recoat speed and increasing the oscillator speed and the rest of the parameters have a nonlinear influence. Results reported in this study can be leveraged to control the porosity of binder jetted parts for applications such as filters, bearings, electronics, and medical implants. |