利用人工神经网络的偏光下热解炭织构类型识别

钟建华 1; 齐乐华 1; 李妙玲 1; 赵志龙 1; 李贺军 2

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利用人工神经网络的偏光下热解炭织构类型识别

更新时间：2025-07-09

- 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
- 西安交通大学学报 2010年44卷第7期页码：46-49+119
- 作者机构：
  
  西北工业大学机电学院,西安,710072
- 作者简介：
- 基金信息：
  
  : 2009-12-27. 作者简介: 钟建华(1983-),男,硕士生;齐乐华(联系人),女,教授,博士生导师. 基金项目: 国家自然科学基金资助项目(50972121);国家自然科学基金重点资助项目(50832004)
- DOI：
  中图分类号： TB332
- 网络首发：2010-07-10，
  
  纸质出版：2010
- 稿件说明：
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钟建华 1, 齐乐华 1, 李妙玲 1, 等. 利用人工神经网络的偏光下热解炭织构类型识别[J]. 西安交通大学学报, 2010,44(7):46-49+119.

Automatic Classification of Pyrocarbon Texture under Polarized Light Microscope Based on Artificial Neural Network[J]. 2010, 44(7): 46-49+119.
钟建华 1, 齐乐华 1, 李妙玲 1, 等. 利用人工神经网络的偏光下热解炭织构类型识别[J]. 西安交通大学学报, 2010,44(7):46-49+119. DOI：

Automatic Classification of Pyrocarbon Texture under Polarized Light Microscope Based on Artificial Neural Network[J]. 2010, 44(7): 46-49+119. DOI：

摘要

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.

Abstract

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.

关键词

Keywords

references

REZNIK B, GUELLALI M, GERTHSEN D. Microstructure and mechanical properties of carbon-carbon composites with multilayered pyrocarbon matrix [J]. Materials Letters, 2002,52(1/2):14-19.

黄伯云,熊翔.高性能炭/炭航空制动材料的制备技术[M].长沙:湖南科学技术出版社,2007:157-170.

PIERSON H O, LIBERMAN M L. The chemical vapor deposition of carbon on carbon fiber [J]. Carbon, 1975,13(3):159-166.

DUPEL P, BOURRAT X, PAILLER R. Structure of pyrocarbon infiltrated by pulse CVI [J]. Carbon, 1995,33(9):1193-1204.

LI Miaoling, QI Lehua, LI Hejun, et al. Measurement of the extinction angle about laminar pyrocarbons by image analysis in reflection polarized light [J]. Materials Science and Engineering: A, 2007, 448(1/2):80-87.

陈强,李贺军,李克智,等.基体改性对碳/碳复合材料烧蚀率影响的神经网络模拟[J].西安交通大学学报,2003,37(3):249-251.

CHEN Qiang, LI Hejun, LI Kezhi, et al. Neural network simulation on effect of burning rate of modified carbon/carbon composites [J]. Journal of Xi'an Jiaotong University, 2003,37(3):249-251.

OBERLIN A. Pyrocarbons [J]. Carbon, 2002,40(1):7-24.

HARILICK R M, SHANMUGAM K. Textural features for image classification [J]. IEEE Trans on Systems, Man, and Cybernetics, 1973,3(6):610-621.

SUN C J, WEE W G. Neighboring gray level dependence matrix for texture classification [J]. Computer Vision, Graphic Image Process, 1983,23:341-352.

LI Aijun, LI Hejun, LI Kezhi, et al. Modeling of CVI process in fabrication of carbon/carbon composites by an artificial neural network [J]. Science in China: E, 2003,46(2):173-181.

GUO Lijie, GAO Jinjin, YANG Jianfeng, et al. Criticality evaluation of petrochemical equipment based on fuzzy comprehensive evaluation and a BP neural network [J]. Journal of Loss Prevention in the Process Industries, 2009,22(4):469-476.

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