journal article·2023-03Open access

An assistive characterized method for growth morphology and orientation of dendrites based on 3D reconstruction technique

Ka Gao, Changjiang Song, Wei Zhao, Hao Wang, Yi Yang, Wei Li, Ping Liu

Abstract

This work describes an aid to three-dimensional (3D) reconstruction based on serial section technology for better characterizing non-transparent material microstructure and orientation. We found that two separate primary Al2Cu phase dendrites of directionally solidified Al–40%Cu alloy appeared in the two-dimensional (2D) microstructure but were shown to be only one dendrite in the three-dimensional (3D) microstructure by using the assistive characterization method. While the reason that separate dendrites appeared was that the remaining liquid phase transformed into the eutectic structures through eutectic reaction, the 3D microstructure results. So the materials microstructure can be obtained and displayed accurately and comprehensively using a 3D reconstruction technique based on serial sections technology. Meanwhile, the [001] growth orientation of the Al2Cu phase dendrite gradually deviated from the heat flow direction during the directional solidification process, and its deviation angle was about 11.42° by EBSD (Electron Backscattered Diffraction) orientation analysis. And the growth orientation deviated from the horizontal direction of the sample by about 2.82°. Interestingly, compared with EBSD, the above similar results could also be simply obtained and indirectly characterized from the 3D reconstruction assistive analysis. Then the above results showed that 3D Reconstruction Technique could be used as an assistive method to easier and more directly represent the materials microstructure and orientation characterization.
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: Solidification and crystal growth phenomena · Aluminum Alloy Microstructure Properties · Metallic Glasses and Amorphous Alloys

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

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