采用中值滤波法提取碳纤维增强树脂基复合材料孔隙形貌特征
Extracting Void Morphology of Carbon Fiber Reinforced Polymer by Midean Filtering
提取孔隙复杂随机形貌特征是建立碳纤维增强树脂基复合材料(Carbon Fiber Reinforced Polymer, CFRP)超声检测模型的前提，本文从图像处理角度出发，针对孔隙率为4.99%、孔隙横向尺寸范围13.7 m~522.6 m的CFRP试样金相照片，将纤维及树脂基体信息看作提取孔隙形貌时的噪声信号，利用Matlab软件对CFRP金相照片进行二值化及中值滤波处理，利用Photoshop图像处理软件进行细化，从而消除噪声信号，得到真实可靠的孔隙形貌。
Extracting the complex and random void morphology is the precondition of setting up ultrasonic inspection model for Carbon Fiber Reinforced Polymer (CFRP). In this paper, image processing is used to study CFRP metallograph, whose porosity is 4.99% and void transverse dimension is 13.7 m~522.6 m. The information of fiber and resin are considered to be noises while extracting void morphology. Binary and median filter realized by Matlab, together with Photoshop refining are use to get rid of noises of CFRP. So that real voids' morphology is acquired.
林莉、刘欢、罗忠兵、丁珊珊
材料科学声学工程
碳纤维增强复合材料孔隙形貌中值滤波
arbon Fiber Reinforced Polymervoid morphologymidean filtering
林莉,刘欢,罗忠兵,丁珊珊.采用中值滤波法提取碳纤维增强树脂基复合材料孔隙形貌特征[EB/OL].(2013-12-19)[2025-08-24].http://www.paper.edu.cn/releasepaper/content/201312-503.点此复制
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