Accesso libero

A Novel Data Mining Approach for Defect Detection in the Printed Circuit Board Manufacturing Process

INFORMAZIONI SU QUESTO ARTICOLO

Cita

Aronszajn, N. (1950). Theory of Reproducing Kernels. Transactions of the American Mathematical Society, 68, 337-404. doi: 10.2307/1990404 Open DOISearch in Google Scholar

Banjoko, A. W., Yahya, W. B., Garba, M. K., & Abdulazeez, K. O. (2019). Weighted support vector machine algorithm for efficient classification and prediction of binary response data. Journal of Physics: Conference Series, 1366. doi: 10.1088/1742-6596/1366/1/012101 Open DOISearch in Google Scholar

Bartova, B., Bina, V., & Vachova, L. (2022). A PRISMA-driven systematic review of data mining methods used for defects detection and classification in the manufacturing industry. Production, 32. doi: 10.1590/0103-6513.20210097 Open DOISearch in Google Scholar

Boser, B. E., Guyon, I. M., & Vapnik, V. N. (1992). A training algorithm for optimal margin classifiers. Proceedings of the Fifth Annual Workshop on Computational Learning Theory, 144-152. doi: 10.1145/130385.130401 Open DOISearch in Google Scholar

Burges, C. (1998). A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery, 2(2), 67-121. doi: 10.1023/A:1009715923555 Open DOISearch in Google Scholar

Chapelle, O., & Schölkopf, B. (2002). Incorporating invariances in non-linear support vector machines. In T. G. Dietterich, S. Becker, & Z. Ghahramani (Eds.), Advances in Neural Information Processing Systems (pp. 594–609). Cambridge, MA: MIT Press. Search in Google Scholar

Chavan, R. R., Chavan, S. A., Dokhe, G. D., Wagh, M. B., & Vaidya, A. S. (2016). Quality Control of PCB using Image Processing. International Journal of Computer Applications, 141(5), 28-32. Search in Google Scholar

Cortes, C., & Vapnik, V.N. (1995). Support-vector networks, Machine Learning, 20, 273-297. doi: 10.1007/BF00994018 Open DOISearch in Google Scholar

Cristianini, N., & Shawe-Taylor, J. (2000). An Introduction to Support Vector Machines and Other Kernel-based Learning Methods. NY Cambridge University Press. doi: 10.1017/CBO9780511801389 Open DOISearch in Google Scholar

Ghosh, A., Guha, T., Bhar, R. B., & Das, S. (2010). Pattern classification of fabric defects using support vector machine. International Journal of Clothing Science and Technology, 23(2/3), 142-151. doi: 10.1108/09556221111107333 Open DOISearch in Google Scholar

Hassanin, A. A. I. M., Abd El-Samie, F. E., & El Banby, G. M. (2019). A real-time approach for automatic defect detection from PCBs based on SURF features and morphological operations. Multimedia Tools and Applications, 78(24), 34437-34457. doi: 10.1007/s11042-019-08097-9 Open DOISearch in Google Scholar

Hu, B., & Wang, J. (2020). Detection of PCB Surface Defects With Improved Faster-RCNN and Feature Pyramid Network. IEEE Access, 8, 108335-108345. doi: 10.1109/ACCESS.2020.3001349 Open DOISearch in Google Scholar

Isa, D., Rajkumar, R., & Woo, K. C. (2007). Pipeline Defect Detection Using Support Vector Machines. 6th WSEAS International Conference on Circuits, Systems, Electronics, Control and Signal Processing, Egypt. Retrieved from http://www.wseas.us/e-library/conferences/2007egypt/papers/568-369.pdf Search in Google Scholar

Kakkar, S., & Narag, A.S. (2007). Recommending a TQM model for Indian organisations. The TQM Magazine, 19(6), 328-353. doi: 10.1108/09544780710756232 Open DOISearch in Google Scholar

Kim, Y.-G., & Park, T.-H. (2020). SMT Assembly Inspection Using Dual-Stream Convolutional Networks and Two Solder Regions. Applied Sciences, 10(13). doi: 10.3390/app10134598 Open DOISearch in Google Scholar

Kumar, P., Shreekanth, T., & Prajwal, M. (2020). Automated Quality Inspection of PCB Assembly Using Image Processing. International Journal of Image, Graphics and Signal Processing, 12(3). doi: 10.5815/ijigsp.2020.03.02 Open DOISearch in Google Scholar

Mahfuz, R. A., M., Hoque, R., Pramanik, B. K., Hamid, E., & Ali Moni, M. (2020). SVM Model for Feature Selection to Increase Accuracy and Reduce False Positive Rate in Falls Detection. doi: 10.1109/IC-4ME247184.2019.9036529 Open DOISearch in Google Scholar

Meyer, D. (2020). Support Vector Machines. Retrieved from https://cran.r-project.org/web/packages/e1071/vignettes/svmdoc.pdf Search in Google Scholar

Mujica, L. E., Vehí, J., Ruiz, M., Verleysen, M., Staszewski, W., & Worden, K. (2008). Multivariate statistics process control for dimensionality reduction in structural assessment. Mechanical Systems and Signal Processing, 22(1), 155-171. doi: 10.1016/j.ymssp.2007.05.001 Open DOISearch in Google Scholar

Reshadat, V., & Kapteijns, R. A. J. W. (2021). Improving the Performance of Automated Optical Inspection (AOI) Using Machine Learning Classifiers. 2021 International Conference on Data and Software Engineering (ICoDSE). doi: 10.1109/ICoDSE53690.2021.9648445 Open DOISearch in Google Scholar

Rokach, L., & Maimon, O. (2006). Data Mining for Improving the Quality of Manufacturing: A Feature Set Decomposition Approach. Journal of Intelligent Manufacturing, 17(3), 285-299. doi: 10.1007/s10845-005-0005-x Open DOISearch in Google Scholar

Shawe-Taylor, J., Bartlett, P.L., Willianmson, R.C., & Anthony, M. (1998). Structural risk minimization over data-dependent hierarchies, IEEE Trans. Information Theory, 44(5), 1926-1940. doi: 10.1109/18. 705570 Open DOISearch in Google Scholar

Soukup, R. (2010). A methodology for optimization of false call rate in automated optical inspection post reflow. doi: 10.1109/ISSE.2010.5547304 Open DOISearch in Google Scholar

Sun, J., Wang, C., Sun, J., & Wang, L. (2013). Analog Circuit Soft Fault Diagnosis based on PCA and PSO-SVM. Journal of Networks, 8(12), 2791-2796. Search in Google Scholar

Suo, H., Li, M., Lu, P., & Yan, Y. (2008). Using SVM as Back-End Classifier for Language Identification. EURASIP Jurnal Audio, Speech, and Music Processing, 674859. doi: 10.1155/2008/674859 Open DOISearch in Google Scholar

Tseng, T.-L., Aleti, K. R., Hu, Z., & Kwon, Y. (2015). E-quality control: A support vector machines approach. Journal of Computational Design and Engineering, 3, 91-101. doi: 10.1016/j.jcde.2015.06.010 Open DOISearch in Google Scholar

Vapnik, V. (1998). The Support Vector Method of Function Estimation. Nonlinear Modelling, 55-85. doi: 10.1007/978-1-4615-5703-6_3 Open DOISearch in Google Scholar

Vapnik, V. N. (1995). The nature of statistical learning theory. New York, USA: Springer-Verlag. Search in Google Scholar

Vapnik, V. N. (1999). An overview of statistical learning theory, IEEE Trans. Neural Networks, 10(5), 988-999. doi: 10.1109/72.788640 Open DOISearch in Google Scholar

Vapnik, V. N., Golowich, S., & Smola, A. (1997). Support vector method for function approximation, regression estimation and signal processing. Advances in Neural Information processing Systems. Cambridge, MA: MIT Press. Search in Google Scholar

Wang, S. yuan, Zhao, Y., & Wen, L. (2016). PCB welding spot detection with image processing method based on automatic threshold image segmentation algorithm and mathematical morphology. Circuit World, 42(3), 97-103. doi: 10.1108/CW-08-2015-0039 Open DOISearch in Google Scholar

Williamson, R. C., Smola, A., & Schölkopf, B. (1999). Entropy numbers, operators and support vector kernels. Cambridge, MA: MIT Press. Search in Google Scholar

Xanthopoulos, P., & Razzaghi, T. (2014). A weighted support vector machine method for control chart pattern recognition. Computers and Industrial Engineering, 70, 134-149. doi: 10.1016/j.cie.2014.01.014 Open DOISearch in Google Scholar

Yang, X., Song, Q., & Wang, Y. (2007). A weighted support vector machine for data classification. International Journal of Pattern Recognition and Artificial Intelligence, 21(5), 961-976. doi: 10.1109/IJCNN.2005.1555965 Open DOISearch in Google Scholar

Yin, Y., Luo, H., Sa, J., & Zhang, Q. (2019). Study and application of improved level set method with prior graph cut in PCB image segmentation. Circuit World, 45(1), 55-64. doi: 10.1108/CW-03-2019-0028 Open DOISearch in Google Scholar

Zakaria, S. S., Amir, A., Yaakob, N., & Nazemi, S. (2020). Automated Detection of Printed Circuit Boards (PCB) Defects by Using Machine Learning in Electronic Manufacturing: Current Approaches. Materials Science and Engineering, 767. doi: 10.1088/1757-899X/767/1/012064 Open DOISearch in Google Scholar

Zhang, C., Chen, X., Chen, M., Chen, S.-C., & Shyu, M.-L. (2005). A multiple instance learning approach for content-based image retrieval using one-class support vector machine. Proceedings of the IEEE International Conference on Multimedia and Expo (ICME ‘05), 1142-1145. doi: 10.1109/ICME.2005.1521628 Open DOISearch in Google Scholar

Zhang, L., Lin, F., & Zhang, B. (2001). Support vector machine learning for image retrieval, Proceedings of the IEEE International Conference on Image Processing (ICIP ‘01), 2, 721-724. doi: 10.1109/ICIP.2001.958595 Open DOISearch in Google Scholar