利用人工神经网络的偏光下热解炭织构类型识别

Automatic Classification of Pyrocarbon Texture under Polarized Light Microscope Based on Artificial Neural Network

针对现有炭/炭复合材料热解炭织构类型的识别方法受人为因素影响较大和操作过程比较复杂等问题,提出了基于热解炭偏光形貌和人工神经网络的热解炭织构类型识别方法.识别时,从炭/炭复合材料的偏光显微图像中分割出热解炭区域,分别用线邻域灰度共生矩阵法和面邻域灰度共生矩阵法提取热解炭的纹理特征,并运用人工神经网络对提取出来的纹理特征进行自动识别,识别率都很高,表明这2类统计纹理特征可以对热解炭织构进行较好的描述.

Aiming at the fact that the existing classification method of the pyrocarbon texture of C/C composites is complex and often affected by human factors,a pyrocarbon texture classification method based on both the artificial neural network （ANN） and the morphologic characters of polarized light microscopy （PLM） image is proposed to get high accuracy. The pyrocarbon area is segmented from PLM image of C/C composite,and the texture characters are extracted applying neighbouring grey level dependence matrixes （NGLDM） and spatial grey level dependence matrixes （SGLDM）. Subsequently,the texture of the pyrocarbon is classified automatically depending on the BP ANN,and the average accuracy gets higher,which shows that this description by the two kinds of statistical characters is effective.