| About this Abstract |
| Meeting |
2022 TMS Annual Meeting & Exhibition
|
| Symposium
|
Powder Materials Processing and Fundamental Understanding
|
| Presentation Title |
Laser-based, Machine-learning Guided, Additive Manufacturing of Ceramics with Designed Microstructure and Hardness |
| Author(s) |
Xiao Geng, Jianan Tang, Dongsheng Li, Yunfeng Shi, Jianhua Tong, Hai Xiao, Fei Peng, Rajendra K. Bordia |
| On-Site Speaker (Planned) |
Rajendra K. Bordia |
| Abstract Scope |
We report ultra–fast sintering of alumina under scanning laser irradiation, and a machine learning approach to predict the microstructure and hardness of sintered alumina. We developed an elegant machine learning (ML) algorithm to predict the microstructure under arbitrary laser power. This algorithm realistically regenerates the SEM micrographs under the trained laser powers. Further, it also accurately predicts the alumina’s microstructure under unexplored laser power. Using this approach, we can simultaneously fabricate a sample array that contains hundreds of individual sample units. Micro-indentation was carried out to measure the hardness of the sample units. The microstructure of selected sample units was characterized. Finally, using these results (of hardness as a function of microstructure), we developed an ML algorithm to not only accurately predict the microstructure of alumina of arbitrary hardness, but also predicts the hardness based on the observed microstructure. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Additive Manufacturing, Ceramics, Machine Learning |