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Geng, Xiao

Publications and source records attributed to Geng, Xiao.

6 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

High-throughput, Ultra-fast Laser Sintering of Ceramics and AI Based Prediction on Processing-Microstructure-Property Relationships

We report high-throughput, ultra-fast laser sintering of alumina sample array and characterization of sample units’ microstructure and hardness, as a fast exploration of laser processing parameters, microstructure, and property. These experimental data were used to train machine-learning (ML) models. Accurate ML predictions were demonstrated for the processing-microstructure-property relationship, specifically in (1) prediction of the microstructure of alumina under arbitrary laser power and (2) prediction of the expected microstructure from the desired hardness. An independent neural network was developed and showed that ML-predicted microstructure had less than 10% error from real ones, in terms of projected hardness. To monitor the microstructure during laser sintering, we demonstrated an ML model that can instantaneously predict ceramic’s microstructure at the laser spot, based on the laser spot brightness. The ML model can generate more than 10 predictions per second, and the error in average grain size was less than 5% from the experimental observations.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Machine-learning-based, online estimation of ceramic’s microstructure upon the laser spot brightness during laser sintering

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. Here, 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).

08 HYDROGEN↗
DOE OSTI2021

Ultra-Fast Laser Fabrication of Alumina Micro-Sample Array and High-Throughput Characterization of Microstructure and Hardness

In the light of recent advances in material informatics, there is a great demand for high-throughput approaches of sample fabrication and property characterization. Currently, no high-throughput approach has been demonstrated for the fast sampling of the microstructure and the correlated properties. In this paper, we demonstrate the ultra-fast fabrication of an alumina sample array and the high-throughput hardness characterization of these sample units. The alumina sample array was fabricated using picosecond (PS) laser micromachining and CO2 laser sintering within a short time (i.e., less than a few minutes). After laser sintering, the hardness of these sample units was characterized using micro-indentation, and the microstructure was observed using scanning electron microscopy (SEM). In each sample unit, the microstructure was uniform for the entire top surface and within about 20 µm depth from the top surface. The relative density (RD) and corresponding micro-hardness of the sample units was found to continuously vary over a wide range from 89% RD with 600 kgf/mm2 hardness to 99% RD with 1609 kgf/mm2 hardness. For these laser-sintered samples, the correlation of hardness and relative density of the alumina matched well with the literature reports on sintered alumina obtained using conventional low-throughput furnace sintering experiments.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Machine learning-based microstructure prediction during laser sintering of alumina

Abstract Predicting material’s microstructure under new processing conditions is essential in advanced manufacturing and materials science. This is because the material’s microstructure hugely influences the material’s properties. We demonstrate an elegant machine learning algorithm that faithfully predicts the microstructure under new conditions, without the need of knowing the governing laws. We name this algorithm, RCWGAN-GP, which is regression-based conditional generative adversarial networks with Wasserstein loss function and gradient penalty. This algorithm was trained with experimental SEM micrographs from laser-sintered alumina under various laser powers. The RCWGAN-GP realistically regenerates the SEM micrographs under the trained laser powers. Impressively, it also faithfully predicts the alumina’s microstructure under unexplored laser powers. The predicted microstructure features, including the morphology of the sintered particles and the pores, match the experimental SEM micrographs very well. We further quantitatively examined the prediction accuracy of the RCWGAN-GP. We trained the algorithm with computer-created micrograph datasets of secondary-phase growth governed by the well-known Johnson–Mehl–Avrami (JMA) equation. The RCWGAN-GP accurately regenerates the micrographs at the trained time series, in terms of the grains’ shapes, sizes, and spatial distributions. More importantly, the predicted secondary phase fraction accurately follows the JMA curve.

08 HYDROGEN↗
DOE OSTI2020

Ultra–fast, selective, non–melting, laser sintering of alumina with anisotropic and size–suppressed grains

In this paper, we report an ultra-fast sintering phenomenon of alumina achieved by the scanning laser irradiation method. Using CO 2 laser irradiation, we found that micrometer-sized alumina powder (d 50 = 1.2 µm) can be sintered close to full density within a few tens of seconds. The microstructure of laser-sintered alumina was different from that of the furnace-sintered alumina. The relative density and grain size of the laser-sintered alumina gradually decreased from the center of the laser beam to the edge. Anisotropy of the grain size was measured along and perpendicular to the scanning direction. This anisotropy decreased as the scanning speed decreased from 0.1 mm/s to 0.01 mm/s. The sintering master curve of grain size versus relative density, which reflects the sintering mechanism, was found to be affected by the laser scanning speed. When the laser scanning speed was 0.1 mm/s, grain size suppression was found for the almost fully dense alumina. However, at lower scanning speed (e.g., 0.01 mm/s), there was significant grain growth in the regions where the relative density was greater than 90%. Furthermore, these results clearly indicate that alumina can be sintered, in the solid-state, to a high density in a short time using scanning laser and the microstructure is different from the furnace-sintered alumina.

08 HYDROGEN↗