| About this Abstract |
| Meeting |
MS&T24: Materials Science & Technology
|
| Symposium
|
Porous Materials for Energy and Environment Applications
|
| Presentation Title |
High Throughput, Ultra-fast Laser Sintering of Ceramics and Glass |
| Author(s) |
Xiao Geng, Jianan Tang, Siddhartha Sarkar, Rajendra K Bordia, Dongsheng Li, Jianhua Tong, Hai Xiao, Fei Peng |
| On-Site Speaker (Planned) |
Fei Peng |
| Abstract Scope |
We report laser sintering of ceramics and glass and machine learning approaches to predict the processing-microstructure-properties relations. Laser sintering allows ultra-fast sintering of ceramics with either porous or fully dense microstructure within a few tens of seconds. The microstructure and density-grain-size trajectory of laser–sintered ceramic are different from those of the furnace–sintered samples. We developed a machine learning (ML) algorithm to predict the microstructure under arbitrary laser power. This algorithm realistically regenerates the SEM micrographs under the trained laser powers. Using ultra-fast laser sintering, we fabricated an alumina sample array that contains ~80 individual sample units, in one laser scan. Due to laser power distribution and the sample location, the individual units in this sample array have different but controllable microstructures. A microstructure-sensitive property, hardness, of the units in a large sample array was measured using micro-indentation. In this way, we established a database of alumina's microstructure with corresponding hardness. |