S200 SiC/SiC Seral Section Data and Fiber Chirality Codes with Tutorial

The contents of this publication include a tutorial to approximate the chirality, or twist, of fibers in a fiber reinforced ceramic matrix composite. There is a sample dataset that contains images of a SiC matrix composite with SiC reinforcement fibers. The MATLAB code performs the velocity gradient analysis and visualizes the results of this analysis. In the near future, the authors intend on publishing a paper about the derivation of the applied methods, which will be titled: "Mesoscale Characterization of Continuous Fiber Reinforced Composites Through Machine Learning: Fiber Chirality".

本出版物包含一则教程，用于近似表征纤维增强陶瓷基复合材料中纤维的手性（chirality）——即其扭转特性。配套提供一份样本数据集，其中包含以碳化硅（SiC）为基体、搭配SiC增强纤维的复合材料图像。所附MATLAB代码可执行速度梯度分析，并将分析结果进行可视化展示。作者团队计划于近期发表一篇阐述所采用方法推导过程的学术论文，论文标题为：《基于机器学习的连续纤维增强复合材料介观表征：纤维手性》（Mesoscale Characterization of Continuous Fiber Reinforced Composites Through Machine Learning: Fiber Chirality）。

搜集汇总

背景与挑战

背景概述

该数据集包含一个教程、SiC基复合材料中SiC增强纤维的图像样本数据以及MATLAB代码，用于近似分析纤维的手性或扭曲，并可视化结果。数据集发布于2019年，专注于陶瓷和复合材料领域，旨在通过机器学习方法进行介观尺度表征。