journal article·2024-12Open access

3D in situ observation of the capillary infiltration of molten silicon in a SiC/SiC composite by X-ray tomography

H. Carpentier, G. Couégnat, O. Caty, A. King, Y. Le Petitcorps, E. Maire, A. Marchais, N. Eberling-Fux

Acta Materialia· Volume 281

Abstract

The Liquid Silicon Infiltration (LSI) process is used to decrease residual porosity of SiC/SiC composite materials. However, it is not fully mastered since the mechanisms involved at 1500 °C under high vacuum are complex to analyze, especially without direct observation. Previous work had demonstrated the feasibility of using X-ray radiography to observe the front rise of silicon in a SiC/SiC composite material during the LSI process. 2D observations, as a first approach, could give a basic understanding of the mechanisms but raised several interrogations due to the superposition of these phenomena along the thickness preventing any quantification. The setup has been improved in order to make X-ray tomography using a fully integrated DC motor in place of the rotation stage commonly used. The sets of X-ray tomographs confirm two successive fillings. First, the molten silicon rapidly and non uniformly invades the accessible intergranular micro porosity of the powder. Then, the liquid slowly fills the remaining isolated powder areas. Once the SiC matrix is fully saturated, the liquid fills the bigger porosities such as cracks and intra yarn macro porosities. In addition, this 3D analysis enabled to give a better comprehension of the non uniform wetting front in the SiC matrix powder. During the 1st step, the accessibility of the powder was known to have a major effect on the speed of progression. Also, the cracks network plays a key role in the filling of the isolated areas in the powder matrix.
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: Advanced ceramic materials synthesis · Advanced Surface Polishing Techniques · Aluminum Alloys Composites Properties

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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