摘要
采用增材制造技术制备的金属三维点阵结构可能存在裂纹、未熔合、断层等缺陷,导致金属点阵结构的结构-功能性能下降,为此提出一种金属三维多层点阵结构内部缺陷的检测方法。在Faster R-卷积神经网络架构基础上设计特征提取网络,结合工业CT扫描图片,对得到的断层灰度图像中缺陷部位进行快速、准确、智能检测识别和定位。实验验证结果表明,对金属三维多层点阵结构样件的内部典型缺陷识别率达到99. 5%.
The cracks,incomplete fusion,faults and other defects may exist in the metal three-dimensional lattice structure prepared by additive manufacturing technology,which lead to the decline of structure-functional performance of metal lattice structure. A Faster R-CNN-based internal defect detection method is proposed for metal three-dimensional multi-layer lattice structure. A feature extraction network is designed on the basis of the Faster R-CNN network architecture. It makes the defects in the obtained gray-scale image and the CT scanning image be detected and positioned quickly,accurately and intelligently. The experimental results show that the recognition rate of the typical internal defects of metal three-dimensional multi-layer lattice structure sample is 99. 5%.
作者
张玉燕
李永保
温银堂
张芝威
ZHANG Yuyan;LI Yongbao;WEN Yintang;ZHANG Zhiwei(School of Electrical Engineering,Yanshan University,Qinhuangdao 066004,Hebei,China;Hebei Province Key Laboratory of Measuring and Testing technologies and Instruments,Yanshan University,Qinhuangdao 066004,Hebei,China)
出处
《兵工学报》
EI
CAS
CSCD
北大核心
2019年第11期2329-2335,共7页
Acta Armamentarii
基金
河北省自然科学基金项目(E2017203240)
