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针对由特殊截面形状的金属棒所组成的理想二维粒状材料,基于数字图像技术,提出了一种细观组构特征分析方法,并编制了程序IPFA,实现了颗粒识别、接触搜索与组构分析等功能。该方法首先对粒状材料试样的原始数字图像进行增强,以消除噪声干扰。在此基础上采用模板匹配技术进行颗粒识别,并将识别结果用于颗粒间的接触搜索,最后给出试样内颗粒长轴方向与接触法线方向等组构特征的统计分析结果。该方法特别适用于由规则颗粒组成的二维粒状材料,识别精度与效率均较高,可作为粒状材料的细观组构特征及其演化规律分析的有效工具。
Aimed at the ideal two-dimensional granular material composed of metal rod with special cross-section shape, a method of meso-structure feature analysis was proposed based on digital image technology. The program IPFA was programmed to realize particle recognition, contact search and structure Analysis and other functions. The method first enhances the original digital image of the granular material sample to eliminate noise interference. On the basis of this, template matching is used for particle identification, and the recognition result is used for the contact search between particles. Finally, the statistical analysis results of the structural features such as the long axis direction and the normal direction of contact in the sample are given. The method is particularly suitable for two-dimensional granular materials composed of regular particles with high recognition accuracy and efficiency, which can be used as an effective tool for the analysis of the microstructure and evolution of granular materials.