| About this Abstract |
| Meeting |
MS&T23: Materials Science & Technology
|
| Symposium
|
Manufacturing and Processing of Advanced Ceramic Materials
|
| Presentation Title |
Machine-Learning-Based, Online Estimation of Ceramic’s Microstructure Upon the Laser Spot Brightness During Laser Sintering |
| Author(s) |
Jianan Tang, Siddhartha Sarkar, Hua Huang, Xiao Geng, Jianhua Tong, Lionel Vargas-Gonzalez, Nicholas Ku, Dongsheng Li, Hai Xiao, Fei Peng |
| On-Site Speaker (Planned) |
Fei Peng |
| Abstract Scope |
The ceramic microstructure strongly influences its properties. During manufacturing, the online monitoring of microstructure is critical to ensure the desired material properties. So far, the microstructure on the relevant scale is usually characterized offline using scanning electron microscopy (SEM), which is time and cost-consuming. In this work, we demonstrate a cost-effective, machine learning (ML)-based approach to simulate the SEM micrographs in real-time from the laser spot brightness. The ML algorithm was a style-based conditional generative adversarial network (CGAN). After training, the ML model could generate high-fidelity microstructure images within 0.1 seconds based on in-situ captured brightness at the laser sintering spot. The ML-predicted microstructures were in good agreement, with less than 5% in difference from the real SEM images. In conclusion, we demonstrate the cost-effective, online microstructure estimation during laser sintering with a simple setup (a camera, a regular computer, and the ML model). |