|About this Abstract
||MS&T21: Materials Science & Technology
||Additive Manufacturing of Ceramic-based Materials: Process Development, Materials, Process Optimization and Applications
||Improving Ceramic Additive Manufacturing via Machine Learning-enabled Closed-Loop Control
||Zhaolong Zhang, Richard D Sisson, Jianyu Liang, Zhaotong Yang
|On-Site Speaker (Planned)
Advanced ceramics are widely used in industries. To achieve the geometric complexity and desirable properties that are difficult to obtain by conventional manufacturing methods, ceramic additive manufacturing (AM) methods have been studied intensively in recent years. However, in-process control with feedback is not currently implemented in any commercially available ceramic three-dimensional (3D) printer. This study employed robocasting, a ceramic AM method, as an example of implementing an in-process control with a feedback loop in a ceramic AM process. In this research, the material parameters, process parameters, machine parameters, and their influences on quality parameters were investigated and identified. A database of the relationships among pressure, extrusion, and the quality of the printed green part was established. A Machine learning model was created based on the established database. Machine learning-enabled closed-loop control was integrated into the current robocasting process to improve the quality of the printed green parts.