OVERVIEW OF AOI USE IN SURFACE-MOUNT TECHNOLOGY CONTROL
Magdalena Michalska
mmagamichalska@gmail.comLublin University of Technology, Department of Electronics and Information Technology (Poland)
https://orcid.org/0000-0002-0874-3285
Abstract
Surface-mount technology is now widely used in the production of printed circuit boards in the electronics industry and has gained many supporters. The miniaturization of electronic components has forced the introduction of machines for visual inspection of assembly correctness, which is more accurate and faster than the human eye, magnifier or microscope. Automatic Optical Inspection (AOI) is a control process that detects defects and errors in the initial PCB manufacturing process. It has become an indispensable element of contract assembly, increasing the quality of services offered and production efficiency. It uses new designs of measuring heads, miniaturization of equipment, software processing the obtained images of boards, and complicated image transformation algorithms.
Keywords:
Automatic Optical Inspection, defect inspection, solder joints, surface-mount technologyReferences
Celik T., Tjahjadi T.: Contextual and variational contrast enhancement. IEEE Transactions on Image Processing 20(12), 2011, 3431–3441.
DOI: https://doi.org/10.1109/TIP.2011.2157513
Google Scholar
Chang C. C., Lin C. J.: LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology 2(3), 2011, 1–27.
DOI: https://doi.org/10.1145/1961189.1961199
Google Scholar
Chang K. H.: Development of optical inspection system for surface mount device light emitting diodes – master thesis. National Sun Yat-sen University, Taiwan 2012.
Google Scholar
Chang W., Su C., Guo D.: Automated optical inspection for the runout tolerance of circular saw blades. Int. J. Adv. Manuf. Technol. 66, 2013, 565–582.
DOI: https://doi.org/10.1007/s00170-012-4350-6
Google Scholar
Colledani M., Tolio T.: Impact of Quality Control on Production System Performance. CIRP Annals - Manufacturing Technology 55(1), 2006, 453–456, [http://doi.org/10.1016/S0007-8506(07)60457-0].
DOI: https://doi.org/10.1016/S0007-8506(07)60457-0
Google Scholar
Dar M., Newman K. E., Vachtsevanos G.: On-line inspection of surface mount devices using vision and infrared sensors. Conference Record Autotestcon’95. Systems Readiness: Test Technology for the 21st Century 1995, 376–384, [http://doi.org/10.1109/AUTEST.1995.522699].
DOI: https://doi.org/10.1109/AUTEST.1995.522699
Google Scholar
Demir D., Birecik S., Kurugollu F., Sezgin M., Bucak I.O., Sankur B., Anarim E.: Quality inspection in PCBs and SMDs using computer vision techniques. 20th Annual Conference of IEEE Industrial Electronics 1994, 857–861 [http://doi.org/10.1109/IECON.1994.397899].
DOI: https://doi.org/10.1109/IECON.1994.397899
Google Scholar
Fang Y. C., Tzeng Y. F., Wu K. Y.: A study of integrated optical design and optimization for LED backlight module with prism patterns. Journal of Display Technology 10(10), 2014, 812–818.
DOI: https://doi.org/10.1109/JDT.2014.2325560
Google Scholar
Gao H., Jin W., Yang X., Kaynak O.: A Line-Based-Clustering Approach for Ball Grid Array Component Inspection in Surface-Mount Technology. IEEE Transactions on Industrial Electronics 64(4), 2017, 3030–3038.
DOI: https://doi.org/10.1109/TIE.2016.2643600
Google Scholar
Garakani A., Michael D. J., Koljonen J.: Automated optical inspection apparatus. US Patent 5, 640, 199, 1997.
Google Scholar
http://www.surfacemountprocess.com/#Circuit layout
Google Scholar
https://www.cherbsloeh.pl/attachments/category/327/Brochure-K3D-Series_EN_Rev06-2017-12.pdf
Google Scholar
Inman R. R., Blumenfeld D. E., Huang N., Li J.: Designing production systems for quality: Research opportunities from an automotive industry perspective. International Journal of Production Research 41(9), 2003, 1953–1971 [http://doi.org/10.1080/0020754031000077293].
DOI: https://doi.org/10.1080/0020754031000077293
Google Scholar
Juha M.: X-ray Machine Vision for Circuit Board Inspection. Conf. of SME, Proc. Vision 86, 1986, 341–355.
Google Scholar
Kim S. E., Jeon J. J., Eom I. K.: Image contrast enhancement using entropy scaling in wavelet domain. Signal Processing 127, 2016, 1–11.
DOI: https://doi.org/10.1016/j.sigpro.2016.02.016
Google Scholar
Kuo C. F. J., Hsu C. T. M., Liu Z. X., Wu H. C.: Automatic inspection system of LED chip using two-stages back-propagation neural network. Journal of Intelligent Manufacturing 25(6), 2015, 1235–1243.
DOI: https://doi.org/10.1007/s10845-012-0725-7
Google Scholar
Kuo C. J., Fang T., Lee C.: Automated optical inspection system for surface mount device light emitting diodes. J. Intell. Manuf. 30, 2019, 641–655.
DOI: https://doi.org/10.1007/s10845-016-1270-6
Google Scholar
Kuo C. J., Tung C., Weng W: Applying the support vector machine with optimal parameter design into an automatic inspection system for classifying micro-defects on surfaces of light-emitting diode chips. J. Intell. Manuf. 30, 2019, 727–741 [http://doi.org/https://doi.org/10.1007/s10845-016-1275-1].
DOI: https://doi.org/10.1007/s10845-016-1275-1
Google Scholar
Langley F. J., Boatright R. R., Crosby L.: Composite electro-optical testing of surface-mount device boards-one manufacturer’s experience, Proceedings: Meeting the Tests of Time, International Test Conference 1989, 686–691 [http://doi.org/10.1109/TEST.1989.82356].
DOI: https://doi.org/10.1109/TEST.1989.82356
Google Scholar
Li Q., Ren S.: A visual detection system for rail surface defects. IEEE Transactions on Systems Man and Cybernetics Part C-Applications and Review 42(6), 2012, 1531–1542.
Google Scholar
Li Q., Ren S.: A visual detection system for rail surface defects. IEEE Transactions on Systems Man and Cybernetics Part C-Applications and Review, 42(6), 2012, 1531–1542.
DOI: https://doi.org/10.1109/TSMCC.2012.2198814
Google Scholar
Lin, H. D.: Automated defect inspection of light-emitting diode chips using neural network and statistical approaches. Expert Systems With Applications 36(1), 2009, 219–226.
DOI: https://doi.org/10.1016/j.eswa.2007.09.014
Google Scholar
Ling‐Yau C., Lawrence Wing‐Tung L.: Total quality control for a surface mount technology process for the manufacture of printed circuit board assemblies, Quality and Reliability Engineering International 11(5), 1995, 325–331 [https://doi.org/10.1002/qre.4680110503].
DOI: https://doi.org/10.1002/qre.4680110503
Google Scholar
Lu S., Zhang X., Kuang Y.: Optimal illuminator design for automatic optical inspection systems. International Journal of Computer Applications in Technology 37(2), 2010.
DOI: https://doi.org/10.1504/IJCAT.2010.032199
Google Scholar
Mahon J., Harris N., Vernon D.: Automated visual inspection of solder paste deposition on surface mount technology PCBs. Elsevier – Computers in Industry, 1989.
DOI: https://doi.org/10.1016/0166-3615(89)90029-8
Google Scholar
Nandi G., Datta S., Bandyopadhyay A., Pal P.K.: Application of PCA-based hybrid Taguchi method for correlated multicriteria optimization of submerged arc weld: A case study. International Journal of Advanced Manufacturing Technology 45(3–4), 2009, 276–286.
DOI: https://doi.org/10.1007/s00170-009-1976-0
Google Scholar
Pang G. K. H, Chu M.: Automated optical inspection of solder paste based on 2.5D visual images. 2009 International Conference on Mechatronics and Automation, Changchun, 2009, 982–987 [http://doi.org/10.1109/ICMA.2009.5246351].
DOI: https://doi.org/10.1109/ICMA.2009.5246351
Google Scholar
Perng D. B., Liu H. W., Chen S. H.: A vision-based LED defect auto-recognition system. Nondestructive Testing and Evaluation 29(4), 2014, 315–331.
DOI: https://doi.org/10.1080/10589759.2014.941841
Google Scholar
Savage R. M., Park H. S., Fan M. S.: Automated inspection of solder joints for surface mount technology. NASA Technical Memorandum 104580, 1993 [http://doi.org/https://ntrs.nasa.gov/citations/19930016948].
Google Scholar
Tsai D. M., Huang T. Y.: Automated surface inspection for statistical textures. Image and Vision Computing 21(4), 2003, 307–323.
DOI: https://doi.org/10.1016/S0262-8856(03)00007-6
Google Scholar
Vanzetti R., Traub A. C.: Combining Soldering with Inspection. IEEE Control Systems Magazine 8(5), 1988, 29–32.
DOI: https://doi.org/10.1109/37.7740
Google Scholar
Watanabe Y.: Automated optical inspection of surface mount components using 2D machine vision. 15th Annual Conference of IEEE Industrial Electronics 3, 1989, 584–589 [http://doi.org/10.1109/IECON.1989.69697].
DOI: https://doi.org/10.1109/IECON.1989.69697
Google Scholar
Wu H. H., Zhang X. M., Kuang Y. C., Lu S. L.: A real-time machine vision system for solder paste inspection. Proceeding of the 2008 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, 205–210.
Google Scholar
Zhao H., Cheng J., Jin J.: NI vision based automatic optical inspection (AOI) for surface mount devices. Devices and method – 2009 International Conference on Applied Superconductivity and Electromagnetic Devices, 356–360 [http://doi.org/10.1109/ASEMD.2009.5306622].
DOI: https://doi.org/10.1109/ASEMD.2009.5306622
Google Scholar
Authors
Magdalena Michalskammagamichalska@gmail.com
Lublin University of Technology, Department of Electronics and Information Technology Poland
https://orcid.org/0000-0002-0874-3285
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