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

ZHANG Yuyan;LI Yongbao;WEN Yintang;ZHANG Zhiwei

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  • Sponsored by: China Association for Science and Technology (CAST)

    Editor-In-Chief: Xu Yida

    ISSN 1000-1093

    　

  • Hosted By: China Ordnance Society

    Published By: Acta Armamentarii

    CN 11-2176/TJ

Acta Armamentarii, a Chinese monthly journal, was first published in 1979. The journal is supervised by China Association for Science and Technology, sponsored by China Ordnance Society，and edited and published by Editorial Board of Acta Armamentarii. The Journal is included in all important domestic databases，and also collected by Engineering Index (EI, USA), Chemical Abstracts (CA, USA), Science Abstracts (SA, UK）, Abstract Journal (AJ, Russia), and Current Bibliography on Science and Technology(CBST).Acta Armamentarii has been selected into the journal Of Ordnance Science and Technology (Weapons Industry) Field Of High Quality Scientific and Technological Periodical Classification Catalogue (2022 edition), ranking T1, and the journal of Automotive Engineering Field of High Quality Scientific and technological Periodical classification Catalogue, ranking T2.
Acta Armamentarii ›› 2019, Vol. 40 ›› Issue (11) : 2329-2335. DOI: 10.3969/j.issn.1000-1093.2019.11.018
Paper

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

  • ZHANG Yuyan1，2， LI Yongbao1，2， WEN Yintang1，2， ZHANG Zhiwei1，2
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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

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ZHANG Yuyan， LI Yongbao， WEN Yintang， ZHANG Zhiwei. Internal Defect Detection of Metal Three-dimensional Multi-layer Lattice Structure Based on Faster R-CNN. Acta Armamentarii. 2019, 40(11): 2329-2335 https://doi.org/10.3969/j.issn.1000-1093.2019.11.018

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

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