Machine-learning-based, Online Estimation of Ceramic’s Microstructure upon the Laser Spot Brightness during Laser Sintering 

Jianan Tang1,3,Email

Siddhartha Sarkar2

Hua Huang2

Xiao Geng2

Jianhua Tong2

Lionel Vargas-Gonzalez4

Nicholas Ku4

Dongsheng Li5

Hai Xiao1,3,Email

Fei Peng2,3,Email

1Department of Electrical and Computer Engineering, Clemson University, Clemson, SC 29634, USA
2Department of Materials Science and Engineering, Clemson University, Clemson, SC, 29634, USA
3Center for Optical Materials Science and Engineering Technologies (COMSET), Clemson University, Anderson, SC 29625, USA
4DEVCOM Army Research Laboratory, Aberdeen Proving Ground, Aberdeen, MD 21005
5Advanced Manufacturing LLC, East Hartford, CT 06108, USA

Abstract

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. We experimentally observed a strong correlation between the laser spot brightness and the corresponding microstructure at the exact locations. The brightness values obtained from thermal emission images and the corresponding SEM micrographs were used in the training datasets. 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. We used the average grain sizes as the metric to evaluate the accuracy of the ML-predicted micrographs. 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).

Machine-learning-based, Online Estimation of Ceramic’s Microstructure upon the Laser Spot Brightness during Laser Sintering