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Automatic characterization of fracture surfaces of AISI 304LN stainless steel using image texture analysis

机译:使用图像纹理分析自动表征AISI 304LN不锈钢的断裂表面

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Texture analyses methods incorporating three-dimensional fractal analysis using box-counting, grey level co-occurrence matrix (GLCM) technique and run length statistical (RLS) analysis have been carried out on tensile fractographs of AISI 304LN austenitic stainless steel for automatic characterization of fracture surfaces. The tensile tests have been carried out at five different strain rates (0.0001, 0.001, 0.01, 0.1 and 1 s~(-1)). The three above mentioned methods, namely, fractal analysis using box-counting, GLCM and RLS analysis are compared in terms of accuracy and computational time and amongst them the run length analysis shows the best result. Eight texture descriptors from the three texture analyses could be extracted to correlate with the observed mechanical properties. Long run emphasis (LRE) and long run high grey level emphasis (LRHGE) depict better correlation among the eight descriptors in this investigation. The results also reveal systematic variation of image texture properties with strain rate.
机译:已在AISI 304LN奥氏体不锈钢拉伸分光仪上进行了结合三维计数,分盒计数,灰度共现矩阵(GLCM)技术和行程长度统计(RLS)分析的纹理分析方法,以自动表征断裂表面。在五个不同的应变率(0.0001、0.001、0.01、0.1和1 s〜(-1))下进行了拉伸试验。比较了上述三种方法,即使用盒计数的分形分析,GLCM和RLS分析的准确性和计算时间,其中运行长度分析显示了最佳结果。可以从三个纹理分析中提取八个纹理描述符,以与观察到的机械性能相关。长期重点(LRE)和长期高灰度级重点(LRHGE)在此研究中描述了八个描述符之间的更好相关性。结果还揭示了图像纹理特性随应变率的系统变化。

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