Design of X-Ray Vision Automatic Testing System

Yue Ping Han; Rui Hong Li

doi:10.4028/www.scientific.net/AMR.898.602

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HomeAdvanced Materials ResearchAdvanced Materials Research Vol. 898Design of X-Ray Vision Automatic Testing System

Design of X-Ray Vision Automatic Testing System

Abstract:

Spatial sampling criteria and fast recognition theory based on single view imaging system are proposed for automatic testing the assembly structures inside products in industry applications. There must be a maximum rotary step for an object within which the least structural size to be tested is ascertained. Rotating the object by the step and imaging it and so on until a 360°turn is completed, an image sequence is obtained that includes the full structural information for recognition. It is verified that objects could be recognized at a single or some limited orientations by analyzing the correlations among the image sequence. The theory is applied to the online automated X-ray vision system. Experiments show that the average identification takes less than 5s with 4.5% of wrong recognition expense.

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Periodical:

Advanced Materials Research (Volume 898)

Pages:

602-605

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.898.602

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Online since:

February 2014

Authors:

Yue Ping Han*, Rui Hong Li

Keywords:

Automatic Test, Fast Recognition, Spatial Sampling Criteria, X-Ray Vision

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* - Corresponding Author

References

[1] Yueping Han, Yan Han, Ruihong Li, Liming Wang, Application of X-ray digital radiography to online automated inspection of interior assembly structures of complex products, Nuclear Instruments and Methods in Physics Research Section A, 604 (2009).

DOI: 10.1016/j.nima.2009.03.201

Google Scholar

[2] V. Cnudde, M.N. Boone, High-resolution X-ray computed tomography in geosciences: A review of the current technology and applications[J], Earth-Science Reviews, 2013, 123: 1–17.

DOI: 10.1016/j.earscirev.2013.04.003

Google Scholar

[3] Curtise K.C. Ng, Zhonghua Sun, Development of an online automatic diagnostic reference levels management system for digital radiography: A pilot experience [J], Computer Methods and Programs in Biomedicine, 2011, 103: 145～150.

DOI: 10.1016/j.cmpb.2010.07.008

Google Scholar

[4] Junguo Bian, Jeffrey H Siewerdsen, Xiao Han, Emil Y Sidky, Jerry L Prince, Charles A Pelizzari, Xiaochuan Pan, Physics in Medicine and Biology, 55 (2010): 6575–6599.

DOI: 10.1088/0031-9155/55/22/001

Google Scholar

[5] Young Jun Roh, Won Shik Park, Hyungsuck Cho, Correcting image distortion in the X-ray digital tomosynthesis system for PCB solder joint inspection, Image and Vision Computing 21 (2003): 1063–1075.

DOI: 10.1016/s0262-8856(03)00117-3

Google Scholar

[6] Dongming Zhao, Songtao Li, A 3D image processing method for manufacturing process automation, Computers in Industry 56 (2005) : 975–985.

DOI: 10.1016/j.compind.2005.05.021

Google Scholar