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复合材料在制造和使用过程中不可避免地会产生褶皱缺陷, 因其形态变化多样, 形变程度较小, 人工辨认存在一定障碍, 容易出现错漏情况. 为提高检测效率, 提出利用Mask-RCNN(Mask region-based convolutional neural networks)目标检测算法对复合材料超声图像中不同形态的褶皱缺陷进行检测并分类. 制备含有不同形态褶皱缺陷的碳纤维复合材料层合板, 利用超声相控阵采集全矩阵数据; 通过波数成像算法得到复合材料层合板纵切面图像, 根据地质层中褶皱的几何学特征, 将复合材料层合板中存在的不同褶皱分为三类, 进而建立褶皱形态与材料损伤程度之间的关系; 提出Mask-RCNN算法用于褶皱缺陷的自动检测并分类, 该算法中语义分割的引入可显示褶皱缺陷的位置和形状. 实验结果表明: Mask-RCNN对不同形态褶皱识别的准确率分别达到92.1％, 90.9％和93.3％, 褶皱分类识别准确、有效. 为实现复合材料层合板数据采集-成像-缺陷判别一体化、自动化提供了理论支撑.Wrinkle defects will be inevitably produced during composite manufacturing and the in-service life of composite structures. Because of their diverse morphological changes and small deformation, it is difficult to manually identify the wrinkle with important errors. In order to improve the inspection efficiency, a Mask-RCNN algorithm is proposed to detect and classify different forms of wrinkle defects in composites based on phased array images. Carbon fiber composite laminates are prepared first in different forms of wrinkle defects. Secondly, the ultrasonic phased array is used to collect full matrix data. The longitudinal scanning image of the composite laminate is then obtained through the wavenumber imaging algorithm. According to the geometric characteristics of the folds in the geological layer, the wrinkles in the composite laminate are divided into three categories, and the relationship between the wrinkle shape and the material damage degree is established. The Mask-RCNN algorithm is finally proposed for automaticaly detecting and classifying the wrinkle defects. The introduction of semantic segmentation in this algorithm can help to reveal the positions and shapes of wrinkle defects. The experimental results show that the accuracies of Mask-RCNN in the recognition of different forms of wrinkles reach 92.1%, 90.9%, and 93.3%, respectively, and the classification and recognition of wrinkles are accurate and effective. It provides theoretical support for the integration and automation of data acquisition-imaging-defect recognition in composite industries.
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Keywords:
- wrinkle /
- wavenumber imaging /
- Mask-RCNN /
- target detection
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图 1 褶皱缺陷自动分类识别流程图
Fig. 1. Flow chart of automatic classification and recognition of wrinkle defects.
图 2 褶皱形态分类 (a) form-Ⅰ类别褶皱形态; (b) form-Ⅱ类别褶皱形态; (c) form-Ⅲ类别褶皱形态
Fig. 2. Classification of wrinkle morphology: (a) Form-Ⅰ category wrinkle morphology; (b) form-Ⅱ category wrinkle morphology; (c) form-Ⅲ category wrinkle morphology
图 6 检测试样及其波数成像 (a) 显微镜图; (b) 波数成像法图; (c) 对图(b)加噪声; (d) 对图(b)调整亮度并沿x轴翻转
Fig. 6. est sample and its wavenumber imaging: (a) Microscope image; (b) wavenumber algorithm; (c) add noise to Figure (b); (d) adjust the brightness of Figure (b) and flip along the x-axis.
图 7 测试图像及其分类结果 (a) 含有一个褶皱的原始图片; (b) 图(a)的测试结果图; (c)含有同一种类多个褶皱的原始图片; (d) 图(c)的测试结果图; (e) 含有多种类多个褶皱的原始图片; (f) 图(e)的测试结果
Fig. 7. Test image and its results: (a) The original picture containing one wrinkle; (b) the test result picture of Figure (a); (c) the original picture containing multiple wrinkles of the same type; (d) the test result picture of Figure (c); (e) the original picture containing multiple types of wrinkles; (f) the test result picture of Figure (e).
表 1 超声相控阵参数设置
Table 1. Parameter settings of ultrasonic phased array.
参数 阵元数 阵元宽度 /mm 阵元
中心距/mm中心频率/MHz 采样频率/MHz 值 32 0.9 1.0 5 50 
下载: 导出CSV
表 2 分类结果的混淆矩阵
Table 2. Classification results confusion matrix.
真实标签 预测标签 类别A 类别B 类别A TP FN 类别B FP TN
下载: 导出CSV
表 3 form-Ⅰ混淆矩阵数据
Table 3. The form-Ⅰ confusion matrix data.
真实标签 预测标签 form-Ⅰ 其他 form-Ⅰ 35 4 其他 3 26
下载: 导出CSV
表 4 form-Ⅱ混淆矩阵数据
Table 4. The form-Ⅱ confusion matrix data.
真实标签 预测标签 form-Ⅱ 其他 form-Ⅱ 10 0 其他 1 23
下载: 导出CSV
表 5 form-Ⅲ混淆矩阵数据
Table 5. The form-Ⅲ confusion matrix data.
真实标签 预测标签 form-Ⅲ 其他 form-Ⅲ 28 3 其他 2 31
下载: 导出CSV
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[1] 赵伟, 刘立武, 孙健, 冷劲松, 刘彦菊 2021 宇航材料工艺 51 73
Google Scholar
Zhao W, Liu L W, Sun J, Leng J S 2021 Aerosp. Mater. Technol. 51 73
Google Scholar
[2] 黎盛寓 2020 塑料科技 48 81
Google Scholar
Li S Y 2020 Plast. Sci. Technol. 48 81
Google Scholar
[3] 陈珍, 李锐 2021 农机使用与维修 06 33
Google Scholar
Chen Z, Li R 2021 Agric. Machinery Using Maintenance 06 33
Google Scholar
[4] 张梦杰, 周春鹏, 陈华辉, 张刚利 2019 中国临床神经外科杂志 24 356
Google Scholar
Zhang M G, Zhou C P, Chen H H, Zhang G L 2019 Chin. J. Clin. Neurosurg. 24 356
Google Scholar
[5] 于晓东, 胡海晓, 贾欲明, 王敏, 曹东风 2020 复合材料学报 37 1932
Google Scholar
Yu X D, Hu H X, Jia Y M, Wang M, Cao D F 2020 Acta Mater. Compos. Sin. 37 1932
Google Scholar
[6] Pandey R K, Sun C T 1999 Compos. Sci. Technol. 59 405
Google Scholar
[7] Hsiao H M, Daniel I M 1996 Compos. Sci. Technol. 56 581
Google Scholar
[8] Hsiao H M, Daniel I M 1996 Composites Part A 27 931
Google Scholar
[9] Alexander C, Kulkarni S 2008 Int. Pipeline Conf. 7 61
[10] 李韦清, 杨涛, 杨冠侠, 刘思南, 杜宇, 刘畅 2018 固体火箭技术 41 621
Google Scholar
Li W Q, Yang T, Yang G X, Liu S N, Du Y, Liu C 2018 J. Solid Rocket Technol. 41 621
Google Scholar
[11] 张婷, 黄爱华, 李向前 2021 航空制造技术 64 78
Google Scholar
Zhang T, Huang A H, Li X Q 2021 Aeronaut. Manuf. Technol. 64 78
Google Scholar
[12] Sandhu A, Reinarz A, Dodwell T J 2018 Composites Part B 205 58
Google Scholar
[13] Koichi M, Yoshihiro M, Akira T, Yoshiro S 2016 Composites Part B 86 84
Google Scholar
[14] 张海燕, 宋佳昕, 任燕, 朱琦, 马雪芬 2021 物理学报 70 060501
Google Scholar
Zhang H Y, Song J X, Ren Y, Zhu Q, Ma X F 2021 Acta Phys. Sin. 70 060501
Google Scholar
[15] 周正干, 李洋, 周文彬 2016 机械工程学报 52 1
Google Scholar
Zhou Z G, Li Y, Zhou W B 2016 Chin. J. Mech. Eng. 52 1
Google Scholar
[16] Zhang H Y, Song J X, Ren Y, Zhu Q, Ma X F 2021 J. Compos. Mater. 56 1
Google Scholar
[17] Callow H J, Hayes M P, Gough P T 2022 Electron. Lett. 38 336
Google Scholar
[18] 于爱暄 2020 硕士学位论文 (大庆: 东北石油大学)
Yu A X 2020 M. S. Thesis (Daqing: Northeast Petroleum University) (in Chinese)
[19] Xie Z K, He F X, Fu S P, Sato I, Tao D C, Sugiyama M 2021 Neural Comput. 33 2163
Google Scholar
[20] Zhang W P, Chen Y G, Yang W M, Wang G J, Xue J H 2021 IEEE Trans. Neural. Networks. Learn. Syst. 32 4742
Google Scholar
[21] Abdulhamit S 2013 Comput. Biol. Med. 43 576
Google Scholar
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