基于数字图像的裂纹测量系统在X80管线钢全尺寸弯曲试验中的验证

张良; 王高峰; 杨锋平; 郭翔; 袁莹涛; 苏鑫

doi:10.6052/j.issn.1000-4750.2021.06.0464

基于数字图像的裂纹测量系统在X80管线钢全尺寸弯曲试验中的验证

doi: 10.6052/j.issn.1000-4750.2021.06.0464

张良^1,,
王高峰^1,,
杨锋平^1,,
郭翔^2,,
袁莹涛^2, ,,
苏鑫^2,

1.
中国石油集团石油管工程技术研究院，西安 710077
2.
西北工业大学航空学院，西安 710072

基金项目: 国家自然科学基金项目(12072279，11602201)

详细信息

作者简介:
张　良(1986−)，男，陕西人，高工，硕士，主要从事油气管道完整性评价与失效分析研究(E-mail: zhangliang008@cnpc.com.cn)

王高峰(1981−)，男，陕西人，高工，硕士，主要从事油气管道完整性评价与性能研究(E-mail: wanggaofeng610@126.com)

杨锋平(1985−)，男，陕西人，高工，博士，主要从事油气管道完整性评价与失效分析研究(E-mail: dragon714cn@163.com)

郭　翔(1984−)，男，陕西人，讲师，博士，主要从事光测实验力学研究(E-mail: guoxiang1984@nwpu.edu.cn)

苏　鑫(1997−)，男，四川人，硕士生，主要从事光测实验力学研究(E-mail: suxin.ds@mail.nwpu.edu.cn)

通讯作者:
袁莹涛(1994−)，男，河南人，博士生，主要从事光测实验力学研究(E-mail: yuanyingtao@mail.nwpu.edu.cn)

中图分类号: V556.5
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- 文章访问数: 177
- HTML全文浏览量: 113
- PDF下载量: 52
- 被引次数: 0
出版历程
- 收稿日期: 2021-06-18
- 修回日期: 2021-09-18
- 网络出版日期: 2021-09-30
- 刊出日期: 2022-11-01

VERIFICATION OF A CRACK MEASUREMENT SYSTEM BASED ON DIGITAL IMAGES IN FULL-SCALE BENDING TEST OF X80 PIPELINE STEEL

1.
CNPC Tubular Goods Research Institute, Xi’an 710077, China
2.
School of Aeronautics, Northwestern Polytechnical University, Xi’an 710072, China

摘要

摘要: 该文提出一种测量结构变形过程中裂纹扩展长度的方法。搭建一种卷积神经网络来抵抗图像中噪声干扰并识别裂纹特征，通过卷积神经网络预测得到裂纹带的初始区域；基于该区域，又提出一种改进的裂纹尖端识别算法来计算裂纹尖端的精确位置坐标；根据位置坐标得到裂纹长度信息。通过增加摄像机的数量，可以同时检测不同位置和方向的裂纹。利用该文提出的方法可以得到裂纹扩展长度与加载信息(如疲劳周期)之间的关系。通过开展中心孔试样疲劳试验和X80管线钢全尺寸弯曲试验，验证了该方法的有效性和准确性。
- 实验力学 /
- 裂纹测量 /
- 光学测量 /
- 图像处理 /
- 卷积神经网络
Abstract: This paper proposes a method to measure the crack propagation length during structural deformation. A convolutional neural network is established to eliminate the interference of surface marks and noises and identify crack features. The initial area of the crack zone is obtained through the prediction of the convolutional neural network. Based on the initial area, an improved crack tip identification algorithm is proposed to calculate the precise position coordinates of the crack tip. According to the position coordinates, the crack length information is obtained. By increasing the number of cameras, cracks in different directions and positions can be detected at the same time. Using this method, the relationship between the crack growth length and the load information (such as fatigue cycles) can be obtained. The effectiveness and accuracy of the method are verified by fatigue tests of the center hole specimen and full-scale bending tests of X80 pipeline steel.
- experimental mechanics /
- crack measurement /
- optical measurement /
- image processing /
- convolutional neural network

HTML全文

图 1 本文提出的方法流程

Figure 1. Flowchart of proposed method

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图 2 裂纹和非裂纹图像示例

Figure 2. Examples of crack and non-crack images

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图 3 提出的CNN架构

Figure 3. Architecture of proposed CNN

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图 4 卷积示例

Figure 4. Example of convolutional layer

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图 5 池化示例

Figure 5. Example of pooling layer

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图 6 CNN检测结果

Figure 6. Results after CNN detection

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图 7 训练过程中训练集和验证集的准确性

Figure 7. Traces of training and validation accuracy

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图 8 Canny检测尖端区域

Figure 8. Canny detector for tip area

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图 9 Sobel与本文方法的检测器比较

Figure 9. Detector comparison of Sobel and proposed method

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图 10 带中心孔的试样

Figure 10. Specimen with a central hole

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图 11 摄像机视图

Figure 11. Camera view

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图 12 中心孔试样裂纹检测结果

Figure 12. Results of crack length measurement for central hole specimen

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图 13 变形实验现场

Figure 13. Experimental environment

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图 14 三维全场轴向应变场测量结果

Figure 14. Results of three-dimensional deformation measurement

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图 15 裂纹扩展长度结果

Figure 15. Results of crack propagation length

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