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Peng, Fei

Publications and source records attributed to Peng, Fei.

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 OSTI2022

Thermal properties of field-assisted-sintered SiCN–Y 2 O 3 composites

Polymer-derived amorphous SiCN has excellent high-temperature stability and properties. To reduce the shrinkage during pyrolysis and to improve the high-temperature oxidation resistance, Y 2 O 3 was added as a filler. In this study, polymer-derived SiCN–Y 2 O 3 composites were fabricated by mixing a polymeric precursor of SiCN with Y 2 O 3 submicron powders in different ratios. The mixtures were cross-linked and pyrolyzed in argon. SiCN–Y 2 O 3 composites were processed using field-assisted sintering technology at 1350°C for 5 min under vacuum. Dense SiCN–Y 2 O 3 composite pellets were successfully made with relative density higher than 98% and homogeneous microstructure. Due to low temperature and short time of the heat-treatment, the grain growth of Y 2 O 3 was substantially inhibited. The Y 2 O 3 grain size was ~1 μm after sintering. The composites’ heat capacity, thermal diffusivity, and thermal expansion coefficients were characterized as a function of temperature. The thermal conductivity of the composites ceramics decreased as the amount of amorphous SiCN increased and the coefficient of thermal expansion (CTE) of the composites increased with Y 2 O 3 content. However, the thermal conductivity and CTE did not follow the rule of mixture. This is likely due to the partial oxidation of SiCN and the resultant impurity phases such as Y 2 SiO 5 , Y 2 Si 2 O 7 , and Y 4.67 (SiO 4 ) 3 O.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Laser 3D printing of highly compacted protonic ceramic electrolyzer stack

Solid oxide electrolysis cells (SOECs) for H 2 production is a core technology for H2@scale. However, its conventional manufacturing methods adapted from the manufacture of solid oxide fuel cells (SOFC) need high cost, especially for small-volume production. The emerging laser 3D printing (L3DP) technology with computer-aided 3D printing and computer-controlled laser processing can fulfill layer-by-layer digital shaping and rapid in-situ consolidating feedstock into complicated geometries. L3DP, a promising additive manufacturing (AM) technology, has achieved significant success in manufacturing plastic and metal parts, which is currently attracting considerable attention for the cost-effective, rapid, and flexible manufacturing of heterogeneous multilayered ceramic devices (e.g., SOECs, SOFCs, and solid-state batteries). It is believed that the L3DP can integrate the advantages of selective consolidation of heterogeneous layers, accurate control of layer microstructures, high processing heat efficiency, high processing speed, high stack compactness, high stack design flexibility, and low stack sealing area for cost-effective, rapid, and flexible manufacturing of SOECs to meet DOE’s electrolyzer target.

08 HYDROGEN↗