PRZEGLĄD WYKORZYSTANIA AOI W PROCESIE KONTROLI MONTAŻU POWIERZCHNIOWEGO

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