| About this Abstract |
| Meeting |
MS&T23: Materials Science & Technology
|
| Symposium
|
Additive Manufacturing of Ceramic-based Materials: Process Development, Materials, Process Optimization and Applications
|
| Presentation Title |
Ultra-fast Laser Sintering of Ceramics and Glasses, and Machine Learning-based, Processing-microstructure-property Predictions for Laser-sintered Ceramics and Glasses |
| Author(s) |
Xiao Geng, Jianan Tang, Siddhartha Sarkar, Yunfeng Shi, Liping Huang, Rajendra K Bordia, Dongsheng Li, Hai Xiao, Fei Peng |
| On-Site Speaker (Planned) |
Fei Peng |
| Abstract Scope |
We report ultra–fast sintering of alumina and silica under scanning laser irradiation, and machine learning predictions of the processing-microstructure-property relationship during laser sintering. Using CO2 laser irradiation, we found that alumina and silica powders can be sintered close to full density within a few tens of seconds. The grain size – density master curve of laser–sintered alumina were different from those of the furnace–sintered alumina. The laser-sintered silica glass exhibits high optical transparency. We developed machine learning models to predict the following processing-microstructure-properties of laser-sintered ceramics: (1) the microstructure under arbitrary laser powers, (2) the microstructure at a laser spot during sintering, (3) the microstructure from a desired hardness and (4) the hardness from microstructure input. |