About this Abstract |
Meeting |
MS&T24: Materials Science & Technology
|
Symposium
|
Manufacturing and Processing of Advanced Ceramic Materials
|
Presentation Title |
Ultra-Fast Laser Sintering of Ceramics and Machine-Learning-Based Prediction on Processing-Microstructure-Property Relationships |
Author(s) |
Xiao Geng, Jianan Tang, Siddhartha Sarkar, Ningxuan Wen, Jianhua Tong, Rajendra K Bordia, Dongsheng Li, Hai Xiao, Fei Peng |
On-Site Speaker (Planned) |
Fei Peng |
Abstract Scope |
We report high-throughput, ultra-fast laser sintering of ceramic sample array and characterization of sample units’ microstructure and hardness, as a fast exploration of laser processing parameters, microstructure, and property. Experimental data were used to train machine-learning (ML) models. Precise ML predictions were demonstrated for the processing-microstructure-property relationship, specifically in (1) prediction of the microstructure of alumina under arbitrary laser power, (2) prediction of the expected microstructure from the desired hardness, and (3) prediction of ceramic’s microstructure at the laser spot, based on the laser spot brightness. |