Localization of component lead inside a THT solder joint for solder defects classification

• Autorzy: Fonseka C. L. S. C. , Jayasinghe J. A. K. S.

Identyfikatory

DOI

10.5604/01.3001.0010.7033

• Języki publikacji: EN

Abstrakty

EN

Purpose: Automatic Optical Inspection (AOI) systems, used in electronics industry have been primarily developed to inspect soldering defects of Surface Mount Devices (SMD) on a Printed Circuit Board (PCB). However, no commercially available AOI system exists that can be integrated to a desktop soldering robotic system, which is capable of identifying soldering defects of Through Hole Technology (THT) solder joints along with the soldering process. In our research, we have implemented an AOI platform that is capable of performing automatic quality assurance of THT solder joints in a much efficient way. In this paper, we have presented a novel approach to identify soldering defects of THT solder joints, based on the location of THT component lead top. This paper presents the methodologies that can be used to precisely identify and localize THT component lead inside a solder joint. Design/methodology/approach: We have discussed the importance of lead top localization and presented a detailed description on the methodologies that can be used to precisely segment and localize THT lead top inside the solder joint. Findings: It could be observed that the precise localization of THT lead top makes the soldering quality assurance process more accurate. A combination of template matching algorithms and colour model transformation provide the most accurate outcome in localizing the component lead top inside solder joint, according to the analysis carried out in this paper. Research limitations/implications: When the component lead top is fully covered by the soldering, the implemented methodologies will not be able to identify the actual location of it. In such a case, if the segmented and detected lead top locations are different, a decision is made based on the direction in which the solder iron tip touches the solder pad. Practical implications: The methodologies presented in this paper can be effectively used to have a precise localization of component lead top inside the solder joint. The precise identification of component lead top leads to have a very precise quality assurance capability to the implemented AOI system. Originality/value: This research proposes a novel approach to identify soldering defects of THT solder joints in a much efficient way based on the component lead top. The value of this paper is quite high, since we have taken all the possibilities that may appear on a solder joint in a practical environment.

Słowa kluczowe

EN

automatic optical inspection; SIFT; SURF; FAST; k-means clustering; contour finding; HSV; HSL; RGI

PL

inspekcja automatyczna optyczna; SIFT; SURF; FAST; klasteryzacja k-średnich

• Wydawca: International OCSCO World Press
• Czasopismo: Journal of Achievements in Materials and Manufacturing Engineering
• Rocznik: 2017
• Tom: Vol. 83, nr 2
• Strony: 57--66
• Opis fizyczny: Bibliogr. 20 poz., rys., tab.

Twórcy

autor

Fonseka C. L. S. C.

• Department of Electronics & Telecommunication, University of Moratuwa, Moratuwa 10400, Sri Lanka

autor

Jayasinghe J. A. K. S.

• Department of Electronics & Telecommunication, University of Moratuwa, Moratuwa 10400, Sri Lanka

Bibliografia

• [1] Soldering robot & technology expert: JAPAN UNIX, https://www.japanunix.com/en/.
• [2] J. Morris, M.J. O’Keefe, Equipment Impacts of Lead Free Wave Soldering, Proceedings of the APEX 2003.
• [3] Ersa Selective Soldering, Systems – Electronics Production Equipment, http://www.ersa.com/media/pdf/prospekte_kataloge/loetmaschinen/selektiv_prospekt_e_web.pdf.
• [4] N.S.S. Mar, Vision-Based Classification of Solder Joint Defects, Master of Engineering (Research) thesis, School of Engineering Systems, Queensland University of Technology, Australia, 2010.
• [5] C.L.S.C. Fonseka, J.A.K.S. Jayasinghe, Colour Model Analysis for Solder Pad Segmentation on Printed Circuit Boards, International Journal of Scientific and Research Publications 6/11 (2016) 212-225.
• [6] P.S. Bradely, U.M. Fayyad, Refining Initial Points for K-Means Clustering, Proceedings of the 15th International Conference on Machine Learning, ICML ’98, 1998, 91-99.
• [7] H.D. Cheng, X.H. Jiang, Y. Sun, J. Wang, Color image segmentation: Advances and prospects, Pattern Recognition 34/12 (2001) 2259-2281.
• [8] J. Bruce, T. Balch, M. Veloso, Fast and inexpensive colour image segmentation for interactive robots, Proceedings of the International Conference on Intelligent Robots and Systems, 2000 IEEE/RSJ, 2000, Vol. 3, 2061-2066.
• [9] G. Bradski, A. Kaehler, Learning OpenCV, O’Reilly, 2008.
• [10] M.R. Maire, Contour Detection and Image Segmentation, PhD thesis, California Institute of Technology, 2003.
• [11] H. Bay, A. Ess, T. Tuytelaars, L. Van Gool, Speeded-Up Robust Features (SURF), Proceedings of the European Conference on Computer Vision, 2006, 404-417.
• [12] N. Zhang, Computing Optimised Parallel Speeded-Up Robust Features (P-SURF) on Multi-Core Processors, International Journal of Parallel Programming 38/2 (2010) 138-158.
• [13] E. Oyallon, J. Rabin, An Analysis of the SURF Method, Image Processing On Line 5 (2015) 176-218.
• [14] D.G. Lowe, Distinctive Image Features from Scale-Invariant Keypoints, International Journal of Computer Vision 60/2 (2004) 91-110.
• [15] E. Rosten, T. Drummond, Machine learning for high-speed corner detection, Proceedings of the Computer Vision – ECCV 2006, 2006, 430-443.
• [16] N. Perveen, D. Kumar, I. Bhardwaj, An Overview on Template Matching Methodologie and its Applications, International Journal of Research in Computer and Communication Technology 2/10 (2013) 988-995.
• [17] A. Kohandani, O. Basir, M. Kamel, A Fast Algorithm for Template Matching, Proceedings of the International Conference Image Analysis and Recognition, 2006, 398-4-9.
• [18] M. Muja, D.G. Lowe, Fast Approximate Nearest Neighbours with Automatic Algorithm Configuration, Proceedings of the International Conference on Computer Vision Theory and Applications, 2009, 331-340.
• [19] H.K. Kelda, P. Kaur, A Review: Color Models In Image Processing, International Journal of Computer Technology and Applications 5/2 (2014) 319-322.
• [20] G. Paschos, Perceptually Uniform Colour Spaces for Colour Texture Analysis: An Empirical Evaluation, IEEE Transactions on Image Processing 10/6 (2001) 932-937.

Uwagi

PL

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).