Abstract

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.

Highlights

  • The correlation between microstructure and properties of ceramics has been studied for years

  • The grain size distribution strongly affects the conductivity of ceramic electrolyte [4,5,6] and the hardness of ceramics [7,8]

  • Different microstructures were obtained for different sample units in the array

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Summary

Introduction

The correlation between microstructure and properties of ceramics has been studied for years. This is because the microstructure has a strong and deterministic effect on certain material properties [1,2,3]. One of the greatest challenges in the ML-guided materials design and prediction is the lack of consistent material’s databases that contain a large quantity of data for ML training. Each heat treatment cycle results in one unique data point in terms of the microstructure. If we want to explore a wide range of microstructures and the resultant properties, conventional furnace heat treatment is inefficient

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