Internal Defects Inspection of Die-cast Parts via the Comparison of X-ray CT Image and CAD Data

CAD 데이터 및 엑스레이 CT이미지 비교를 통한 다이캐스팅 부품의 내부 결함 검사방법

  • 홍경택 (스코프비전) ;
  • 심재홍 (한국산업기술대학교 메카트로닉스공학과)
  • Received : 2018.02.19
  • Accepted : 2018.03.23
  • Published : 2018.03.31

Abstract

Industrially, die-casting products are formed through casting, and so the methods to inspect the defects inside them are very restrictive. External inspection methods including visual inspection, sampling judgment, etc. enables researchers to inspect possible external defects, but x-ray inspection equipment has been generally used to inspect internal ones. Recently, they have been also applying three-dimensional internal inspections using CT equipment. However, they have their own limitations in applying to the use of industrial inspection due to limited detection size and long calculation time. To overcome the above problems, this paper has suggested a method to inspect internal defects by comparing the CAD data of the product to be inspected with the 3D data of the CT image. In this paper, we proposed a method for fast and accurate inspection in three dimensions by applying x-ray inspection to find internal defects in industrial parts such as aluminum die casting products. To show the effectiveness of the proposed method, a series of experiments have been carried out.

Keywords

References

  1. Ji H.R. and Hong H., "Automatic detection of internal and surface cracks of metal products using two and three-dimensional crack enhancement filtering methods in industrial CT volume data", J. of KISE, 41(1), pp. 67-79, (2014).
  2. Bi B., Zeng L., Jiang H., "A novel method for 3D crack edge extraction in CT volume data", X-ray Science and Technology, 19, pp.429-442, (2011).
  3. Ehrig K., Goebbles J., Meinel D., Paetsch O., Prohaska S., Zobel V., "Comparison of crack detection methods for analyzing damage processes in concrete with computed tomography," International Symposium on Digital Industrial Radiology and Computed Tomography, pp.1-8, (2011).
  4. Feldkamp L., Davis L., Kress J., "Practical cone-beam algorithm," J. Opt. Soc. Am., 1(6), pp. 612-619, (1984). https://doi.org/10.1364/JOSAA.1.000612
  5. Kak A. C. and Slaney M., Principles of Computerized Tomographic Imaging, IEEE Press, (1999).
  6. Song D.G., Jeong C.W., Jun K.S., "The study on the implementation of the X-ray CT system using the conebeam for the 3D dynamic image acquisition", J. of Advanced Information Technology and Convergence, 9(2), pp. 57-64, (2011).
  7. Yoon H. and Yun S., "Development of Graphical Solution for Computer-Assisted Fault Diagnosis: Preliminary Study", J of Korean Society for Nondestructive Testing, 29(1), pp. 36-42, (2009).
  8. Kim Y.H. and Nam J.H., "Estimation of the noise variance in image and noise reduction", J. of the Korean Statistical Society, pp. 905-914, (2011).
  9. Choi H.H., Introduction of 3D Game Programming using the DIRECTX 9, PART II - Chapter 2, Jeongbo Publishing, (2004).
  10. Yoo S.K. and Kim N.H., "Three dimensional reconstruction and display of CT images via linear octree", J. of the Institute of Electronics and Information Engineers, 26(6), pp. 876-885, (1989).
  11. William E.L. and Harvey E.C., "Marching cube: a high resolution 3D surface construction algorithm", Computer Graphics, 21(4), (1987).
  12. Kang D.S. and Shin B.S., "Real-time volume rendering using point-primitive", J. of Korea Multimedia Society, 14, pp. 1229-1237, (2011). https://doi.org/10.9717/kmms.2011.14.10.1229
  13. Eberly D., "Triangulation by ear clipping", Geometric Tools, LLC, (1998).
  14. Woo S.B., A Retrieval Method for 3D CAD Data Using Sectional Image, M.D. Thesis, Hanyang University Graduate School, (2004).
  15. Yoon D.M. and Han J.H., "Connected component labeling-based geometric features and a classification algorithm for surface defect inspection", J. of KISE, 21(5), pp. 739-749, (1994).