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。
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.
作者
张良
王高峰
杨锋平
郭翔
袁莹涛
苏鑫
ZHANG Liang;WANG Gao-feng;YANG Feng-ping;GUO Xiang;YUAN Ying-tao;SU Xin(CNPC Tubular Goods Research Institute,Xi’an 710077,China;School of Aeronautics,Northwestern Polytechnical University,Xi’an 710072,China)
出处
《工程力学》
EI
CSCD
北大核心
2022年第11期157-165,共9页
Engineering Mechanics
基金
国家自然科学基金项目(12072279,11602201)。
关键词
实验力学
裂纹测量
光学测量
图像处理
卷积神经网络
experimental mechanics
crack measurement
optical measurement
image processing
convolutional neural network
