Daxing Zhao
School of Mechanical Engineering, Hubei University of Technology, Wuhan, 430068, China
Wei Feng
School of Mechanical Engineering, Hubei University of Technology, Wuhan, 430068, China
Guodong Sun
School of Mechanical Engineering, Hubei University of Technology, Wuhan, 430068, China
Yu Peng
School of Mechanical Engineering, Hubei University of Technology, Wuhan, 430068, China
ABSTRACT
Micro-electronic connector is widely used in electronic products and electrical equipments. The traditional quality detection method for micro-electronic connector relies on manual inspection, which is inefficient and susceptible to subjective factors. A high precision measurement system of micro-electronic connector has been developed based on machine vision in order to improve detection efficiency and measurement accuracy. Firstly the system architecture for high precision measurement is presented. Then the segmented image acquisition by cameras is introduced and unique ROI setting function of this system can realize the detection and measurement for various types of micro-electronic connectors. Finally an algorithm based on image processing is designed to achieve seamless splicing of segmental images. The experiments have proved the defect recognition rate of proposed system can surpass 95% and the measurement accuracy reaches 0.007 mm. The system performance can meet the high precision measurement requirements of micro-electronic connectors. Therefore this high precision measurement method can save the human resources and ensure the quality of micro-electronic connectors.
PDF References
Received: August 06, 2013;
Accepted: October 09, 2013;
Published: November 13, 2013
How to cite this article
Daxing Zhao, Wei Feng, Guodong Sun and Yu Peng, 2013. High Precision Measurement System of Micro-electronic Connector based on Machine Vision. Journal of Applied Sciences, 13: 5363-5369.
DOI: 10.3923/jas.2013.5363.5369
URL: https://scialert.net/abstract/?doi=jas.2013.5363.5369
DOI: 10.3923/jas.2013.5363.5369
URL: https://scialert.net/abstract/?doi=jas.2013.5363.5369
REFERENCES
- Chapman, K.W., W.C. Johnson and T.J. McLean, 1990. A high speed statistical process control application of machine vision to electronics manufacturing. Comput. Ind. Eng., 19: 234-238.
CrossRefDirect Link - Chen, Z.L., B.J. Zou, M.Z. Huang, H.L. Shen and G.J. Xin, 2012. Influence of intensity feature on ROI extraction. J. Central South Univ. (Sci. Technol.), 43: 208-214.
Direct Link - Deka, B. and S. Choudhury, 2013. A multiscale detection based adaptive median filter for the removal of salt and pepper noise from highly corrupted images. Int. J. Signal Process. Image Process. Pattern Recogn., 6: 129-144.
Direct Link - Dong, C.B., X.J. Hu, Y. Wang and L.M. Fu, 2013. Realization of car's frontal projected area based on openCV. Applied Mech. Mater., 300-301: 1673-1676.
CrossRefDirect Link - Gordon, J.A. and D.R. Novotny, 2012. Simultaneous imaging and precision alignment of two mm wave antennas based on polarization-selective machine-vision. IEEE Trans. Instrum. Measure., 61: 3065-3071.
CrossRefDirect Link - Li, Y., Y.F. Li, Q.L. Wang, D. Xu and M. Tan, 2010. Measurement and defect detection of the weld bead based on online vision inspection. IEEE Trans. Instrum. Measure., 59: 1841-1849.
CrossRefDirect Link - Aoyagi, M., T. Ueno and M. Okuda, 2009. Automatic detection system for complete connection of a waterproof soft-shell electronic connector with a sliding locking device. IEEE Sensors J., 9: 285-292.
CrossRef - Park, J.J., J.K. Kim, E.S. Lee and M.K. Lee, 2011. Micro circular path measurement of two-axis stage using a machine vision system and the application. Int. J. Adv. Manuf. Technol., 56: 1049-1055.
CrossRef - Cubero, S., N. Aleixos, E. Molto, J. Gomez-Sanchis and J. Blasco, 2011. Advances in machine vision applications for automatic inspection and quality evaluation of fruits and vegetables. Food Bioprocess Technol., 4: 487-504.
CrossRef