基于数字图像处理技术的热障涂层形貌特征识别方法

doi:10.12066/j.issn.1007-2861.2371

上海大学学报(自然科学版) ›› 2022, Vol. 28 ›› Issue (3): 523-533.doi: 10.12066/j.issn.1007-2861.2371

• 材料微结构图像识别与微结构分析 • 上一篇下一篇

基于数字图像处理技术的热障涂层形貌特征识别方法

刘宇虹¹, 韩越兴¹^,²(), 汪语嫣³, 曾毅³

1.上海大学计算机工程与科学学院, 上海 200444
2.之江实验室, 浙江杭州 311100
3.中国科学院上海硅酸盐研究所高性能陶瓷与超微结构国家重点实验室, 上海 200050

收稿日期:2022-03-20 出版日期:2022-06-30 发布日期:2022-05-27
通讯作者: 韩越兴 E-mail:han_yx@i.shu.edu.cn
作者简介:韩越兴(1975—), 男, 副教授, 博士生导师, 博士, 研究方向为图像处理、计算机视觉等. E-mail: han_yx@i.shu.edu.cn
基金资助:
国家重点研发计划资助项目(2018YFB0704400);国家重点研发计划资助项目(2020YFB0704503);上海市自然科学基金资助项目(20ZR1419000);之江实验室科研攻关资助项目(2021PE0AC02)

Recognition of topographic features of thermal barrier coating based on image processing

LIU Yuhong¹, HAN Yuexing¹^,²(), WANG Yuyan³, ZENG Yi³

1. School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China
2. Zhejiang Laboratory, Hangzhou 311100, Zhejiang, China
3. The State Key Lab of High Performance Ceramics and Superfine Microstructure, Shanghai Institute of Ceramics, Chinese Academy of Science, Shanghai 200050, China

Received:2022-03-20 Online:2022-06-30 Published:2022-05-27
Contact: HAN Yuexing E-mail:han_yx@i.shu.edu.cn

摘要/Abstract

摘要：

针对人工检测热障涂层形貌特征所具有的繁复性、误差大等缺点, 提出一种利用机器视觉自动化识别热障涂层形貌特征并计算形貌特征参数的方法. 完成了基于数学形态学的片层轮廓自动提取及铺展形貌参数的计算, 用最大类间方差法求取二值分割阈值, 运用均值滤波和形态学操作为图片去噪并保证单个片层的连通性, 通过轮廓提取来获得片层边缘信息, 最后根据所提取出的轮廓计算片层的实度参数. 同时, 进一步完成了基于遍历搜索的热障涂层中裂纹的自动识别及长度计算. 首先, 识别出图像中的片层并去除, 运用闭运算完成断裂裂纹的修复, 通过图像细化得到裂纹骨架; 然后, 遍历搜索每条裂纹, 完成长度计算. 结果表明, 采用所提出方法检测片层轮廓和识别裂纹的效果良好, 具有较好的抗噪声干扰能力, 可以精确计算出形貌特征参数, 对研究热喷涂熔滴在基材表面的沉积行为有重要的推动作用.

关键词: 热障涂层, 机器视觉, 阈值分割, 数学形态学

Abstract:

To address the shortcomings of manual detection of thermal barrier coating topographic features, such as complexity and large errors, a method for automatically identifying topographic features of thermal barrier coatings using machine vision and calculating topographic feature parameters is proposed in this study. First, splat contours are automatically extracted based on a mathematical morphology and calculation of spread morphological parameters. The maximum interclass variance method is next used to obtain the binary segmentation threshold and the median filter and morphological operations are used to denoise the image and ensure a single splat. The connectivity of the splat is then obtained by contour extraction, and the solidity parameter of the splat is finally calculated according to the extracted contour. Simultaneously, this study realizes automatic identification and length calculation of cracks in thermal barrier coatings based on a traversal search. First, the lamellae in the image are identified and removed, and the fractured crack is repaired by the closing operation. The cracked skeleton is next obtained through image refinement, and each crack is then traversed and searched to complete the length calculation. The results show that the method effectively detects the splat profile and identifies cracks, has a good anti-noise interference ability, and can accurately calculate topographic feature parameters. Thus, this method can play a critical role in promoting the study of the deposition behavior of thermal spray droplets on the surfaces of substrates.

Key words: thermal barrier coating, machine vision, threshold segmentation, mathematical morphology

中图分类号:

TP 304

刘宇虹, 韩越兴, 汪语嫣, 曾毅. 基于数字图像处理技术的热障涂层形貌特征识别方法[J]. 上海大学学报(自然科学版), 2022, 28(3): 523-533.

LIU Yuhong, HAN Yuexing, WANG Yuyan, ZENG Yi. Recognition of topographic features of thermal barrier coating based on image processing[J]. Journal of Shanghai University（Natural Science Edition）, 2022, 28(3): 523-533.

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基于数字图像处理技术的热障涂层形貌特征识别方法

Recognition of topographic features of thermal barrier coating based on image processing

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