Internal Defect Detection of Metal Three-dimensional Multi-layer Lattice Structure Based on Faster R-CNN

doi:10.3969/j.issn.1000-1093.2019.11.018

Abstract

Abstract: 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%. Key

Key words: metallatticestructure, defectdetection, non-destructivetesting, CTscanningimage, FasterR-convolutionalneuralnetwork

CLC Number:

TG115.281

ZHANG Yuyan， LI Yongbao， WEN Yintang， ZHANG Zhiwei. Internal Defect Detection of Metal Three-dimensional Multi-layer Lattice Structure Based on Faster R-CNN[J]. Acta Armamentarii, 2019, 40(11): 2329-2335.

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第40卷
第11期2019 年11月兵工学报ACTA
ARMAMENTARIIVol.40No.11Nov.2019