{"title":"Deep learning-based microstructure analysis of multi-component heterogeneous composites during preparation","authors":"","doi":"10.1016/j.compositesa.2024.108437","DOIUrl":null,"url":null,"abstract":"<div><p>Monitoring microstructure evolution during the preparation has always been a difficult problem in the modification studies of SiC composite matrix. Here, we used X-ray tomography microscopy to observe the microstructure of SiC<sub>f</sub>/SiC-W-ZrB<sub>2</sub> composites at different fabrication stage. Based on deep learning, the tracking of the densification process of matrix-modified SiC<sub>f</sub>/SiC composites was achieved and its suitability for microstructure reconstruction was also verified. The results showed that the average errors of reconstructed SiC<sub>f</sub>/SiC, pore and Metal (W/ZrB<sub>2</sub>) are respectively 7.53%, 8.31% and 0.96% by comparison with the segmentation results. Compared with the experimental results, the average error and the average relative error of reconstructed SiC<sub>f</sub>/SiC is less than 3% and 3.74%.</p></div>","PeriodicalId":282,"journal":{"name":"Composites Part A: Applied Science and Manufacturing","volume":null,"pages":null},"PeriodicalIF":8.1000,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Composites Part A: Applied Science and Manufacturing","FirstCategoryId":"1","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1359835X24004342","RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, MANUFACTURING","Score":null,"Total":0}
引用次数: 0
Abstract
Monitoring microstructure evolution during the preparation has always been a difficult problem in the modification studies of SiC composite matrix. Here, we used X-ray tomography microscopy to observe the microstructure of SiCf/SiC-W-ZrB2 composites at different fabrication stage. Based on deep learning, the tracking of the densification process of matrix-modified SiCf/SiC composites was achieved and its suitability for microstructure reconstruction was also verified. The results showed that the average errors of reconstructed SiCf/SiC, pore and Metal (W/ZrB2) are respectively 7.53%, 8.31% and 0.96% by comparison with the segmentation results. Compared with the experimental results, the average error and the average relative error of reconstructed SiCf/SiC is less than 3% and 3.74%.
期刊介绍：
Composites Part A: Applied Science and Manufacturing is a comprehensive journal that publishes original research papers, review articles, case studies, short communications, and letters covering various aspects of composite materials science and technology. This includes fibrous and particulate reinforcements in polymeric, metallic, and ceramic matrices, as well as 'natural' composites like wood and biological materials. The journal addresses topics such as properties, design, and manufacture of reinforcing fibers and particles, novel architectures and concepts, multifunctional composites, advancements in fabrication and processing, manufacturing science, process modeling, experimental mechanics, microstructural characterization, interfaces, prediction and measurement of mechanical, physical, and chemical behavior, and performance in service. Additionally, articles on economic and commercial aspects, design, and case studies are welcomed. All submissions undergo rigorous peer review to ensure they contribute significantly and innovatively, maintaining high standards for content and presentation. The editorial team aims to expedite the review process for prompt publication.
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