A framework for inspection of dies attachment on PCB utilizing machine learning techniques

References

• Alahi, A., Ortiz, R., & Vandergheynst, P. (2012). Freak: Fast retina keypoint. IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
   Google Scholar
• Baccarini, L. M. R., Silva, V. V. R., Menezes, B. R., & Caminhas, W. M. (2011). SVM practical industrial application for mechanical faults diagnostics. Expert Systems with Applications, 38(6), 6980–6984. doi: 10.1016/j.eswa.2010.12.017
   Google Scholar
• Beck, M., & Clark, D. (1991). SMT inspection strategies: Maximizing cost effectiveness. Proceedings of the Technical Program: NEPCON West, 91, 1075–1081.
   Google Scholar
• Bond, J. (1991). Inspection systems distribute test throughout manufacturing, Test Meas. World Dec. 1991, 65–66.
   Google Scholar
• Borthakur, B., Lante, A., & Kulkarni, P. (2015). A comparative study on automated PCB defect detection algorithms and to proposed an optimal approach to improve the technique. International Journal of Computer Applications, 114(6), 27–33. doi: 10.5120/19985-1938
   Google Scholar
• Boser, B. E., Guyon, I. M., & Vapnik, V. N. (1992). A training algorithm for optimal margin classifiers. 5th Annual Workshop on Computational Learning Theory, ACM, 144–152.
   Google Scholar
• Breiman, L. (1999). Random forests – random features (Technical Report 567). Berkeley: Statistics Department, University of California. Retrieved from ftp://ftp.stat.berkeley.edu/pub/users/breiman.
   Google Scholar
• Calonder, M., Lepetit, V., Strecha, C., & Fua, P. (2010). BRIEF: Binary robust independent elementary features. European Conference on Computer Vision (ECCV).
   Google Scholar
• Canny, J. (1986). A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 8, 679–698. doi: 10.1109/TPAMI.1986.4767851
   Google Scholar
• Chen, S. H., & Perng, D. B. (2014). A molding surface auto-inspection system. Journal of Industrial and Manufacturing Engineering, 8(5), 949–953.
   Google Scholar
• Cheng, Y., Chen, K., Sun, H., Zhang, Y., & Tao, F. (2018). Data and knowledge mining with big data towards smart production. Journal of Industrial Information Integration, 9. doi: 10.1016/j.jii.2017.08.001
   Google Scholar
• Duan, G., Wang, H., Liu, Z., & Chen, Y.-W. (2012). A machine learning-based framework for automatic visual inspection of microdrill bits in PCB production. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Application and Reviews), 42(6), 1679–1689. doi: 10.1109/TSMCC.2012.2216260
   Google Scholar
• Duan, L., & Xu, L. D. (2012). Business intelligence for enterprise systems: A survey. IEEE Transactions on Industrial Informatics, 8(3), 679–687. doi: 10.1109/TII.2012.2188804
   Google Scholar
• Fawcett, T. (2006). An introduction to roc analysis. Pattern Recognition Letters, 27(8), 861–874. doi: 10.1016/j.patrec.2005.10.010
   Google Scholar
• Friedman, J. H. (1999). Stochastic gradient boosting. Computational Statistics and Data Analysis, 38, 367–378. doi: 10.1016/S0167-9473(01)00065-2
   Google Scholar
• Gerald, J. (1992). Advances in board inspection. Evaluation Engineering, 126–133.
   Google Scholar
• Hartley, R. I., & Zisserman, A. (2004). Multiple view geometry in computer vision (2nd ed). Cambridge: Cambridge University Press.
   Google Scholar
• Hilbe, J. M. (2014). International encyclopedia of statistical science. Berlin: Springer.
   Google Scholar
• Jiang, B. C., Wang, C. C., & Huang, W. H. (2008). Evaluation of a digital camera image applied to PCB inspection. Human Factors and Ergonomics in Manufacturing & Service Industries, 18(4), 424–437. doi: 10.1002/hfm.20100
   Google Scholar
• Lee, S. J., & Siau, K. (2001). A review of data mining techniques. Industrial Management & Data Systems, 101(1), 41–46. doi: 10.1108/02635570110365989
   Google Scholar
• Mar, N. S. S., Yarlagadda, P. K. D. V., & Fookes, C. (2011). Design and development of automatic visual inspection system of PCB manufacturing. Robotics and Computer – Integrated Manufacturing, 27(5), 949–962. doi: 10.1016/j.rcim.2011.03.007
   Google Scholar
• Oyeleye, O., & Lehtihet, E. A. (1999). Automatic visual inspection of surface mount solder joint defects. International Journal of Production Research, 37(6), 1217–1242. doi: 10.1080/002075499191229
   Google Scholar
• Rau, H., & Wu, C. H. (2005). Automatic optical inspection for detecting defects on printed circuit board inner layers. International Journal of Advanced Manufacturing Technology, 25(9), 940–946. doi: 10.1007/s00170-004-2299-9
   Google Scholar
• Rosenblatt, F. (1961). Principles of neurodynamics: Perceptrons and the theory of brain mechanisms. Washington, DC: Spartan Books.
   Google Scholar
• Rublee, E., Rabaud, V., Konolige, K., & Bradski, G. R. (2011). ORB: An efficient alternative to SIFT or SURF. IEEE International Conference on Computer Vision (ICCV).
   Google Scholar
• Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning internal representations by error propagation. Parallel distributed processing: Explorations in the microstructure of cognition, vol 1, MIT Press.
   Google Scholar
• Savage, R. M. (1991). NASA evaluates automated inspection systems, Test and Measurement World, 59–64.
   Google Scholar
• Sutherland, I. E. (1974). Three-dimensional data input by tablet. Proceedings of the IEEE, 62, 453–461. doi: 10.1109/PROC.1974.9449
   Google Scholar
• Suzuki, S., & Abe, K. (1985). Topological structural analysis of digitized binary images by border following. Computer Vision, Graphics, and Image Processing, 30, 32–46. doi: 10.1016/0734-189X(85)90016-7
   Google Scholar
• Vafeiadis, T., Ioannidis, D., Ziazios, C., Metaxa, I. N., & Tzovaras, D. (2017). Towards robust early stage data knowledge-based inference engine to support zero-defect strategies in manufacturing environment. 27th International Conference on Flexible Automation and Intelligent Manufacturing (FAIM 2017), Modena, Italy.
   Google Scholar
• Wang, X., Chen, X., & Bi, Z. (2015). Support vector machine and ROC curves for modeling of aircraft fuel consumption. Journal of Management Analytics, 2(1), 22–34. doi: 10.1080/23270012.2015.1019582
   Google Scholar
• Wu, C. H., Wang, D. Z., Ip, A., Wang, D. W., Chan, C. Y., & Wang, H. F. (2009). A particle swarm optimization for components placement inspection on printed circuit boards. Journal of Intelligent Manufacturing, 20, 535–549. doi: 10.1007/s10845-008-0140-2
   Google Scholar
• Yuan, R., Li, Z., Guan, X., & Xu, L. (2010). An SVM-based machine learning method for accurate internet traffic classification. Information Systems Frontiers, 12(2), 149–156. doi: 10.1007/s10796-008-9131-2
   Google Scholar