Computational intelligence in mass soldering of electronics - A survey

Expert Systems with Applications

Volumn 39, Issue 10, 2012, Pages 9928-9937

  Liukkonen, Mika ^a   Havia, Elina ^b   Hiltunen, Yrjö ^a  

^a UNIVERSITY OF EASTERN FINLAND   (Finland)

^b 3K Factory of Electronics ^*  (Finland)

Author keywords

Artificial neural network; Computational intelligence; Electronics; Soldering

Indexed keywords

APPLICATION FIELDS; DATA ACQUISITION SYSTEM; ELECTRONIC COMPONENT; ELECTRONICS PRODUCTION; IN-PROCESS; INDUSTRIAL PROBLEM; INTELLIGENT METHOD; KEY PROCESS; MASS SOLDERING; PRODUCT VARIETY; PRODUCTION ENVIRONMENTS; REAL-WORLD PROBLEM; SELF-ORGANIZE; TIME-BASED COMPETITION;

ARTIFICIAL INTELLIGENCE; ELECTRONIC EQUIPMENT; INFORMATION TECHNOLOGY; MANUFACTURE; NEURAL NETWORKS; SURVEYS;

SOLDERING;

EID: 84859217082     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2012.02.100     Document Type: Review

References (81)

1. G. Acciani, G. Brunetti, and G. Fornarelli Application of neural networks in optical inspection and classification of solder joints in surface mount technology IEEE Transactions on Industrial Informatics 2 2006 200 209
2. G. Acciani, G. Brunetti, and G. Fornarelli A multiple neural network system to classify solder joints on integrated circuits International Journal of Computational Intelligence Research 2 2006 337 348
3. C.R. Alavala Fuzzy logic and neural networks: Basic concepts and applications 2008 New Age International Publishers New Delhi
4. D. Barbini, and P. Wang Implementing lead-free wave soldering: Higher levels of copper and iron can change the alloy and require new guidelines Printed Circuit Design & Manufacture 2005
5. Boser, B.; Guyon, I.; Vapnik, V. N. (1992). A Training Algorithm for Optimal Margin Classifiers. In: D. Haussler (Ed.), Proceedings of the fifth annual workshop on computational learning theory (pp. 144-152). ACM Press.
6. L. Breiman, J.H. Friedman, R.A. Olshen, and C.J. Stone Classification and regression trees 1984 Wadsworth Inc Monterey, CA
7. D.S. Broomhead, and D. Lowe Multivariable functional interpolation and adaptive network Complex Systems 2 1988 321 355
8. Cho, H.; Park, W. S. (2002). Neural Network Applications in Automated Optical Inspection: State of the Arts. In B. Javidi & D. Psaltis (Eds.), Algorithms and Systems for Optical Information Processing VI, Proceedings of SPIE, Vol. 4789 (pp. 224-236).
9. A.K. Choudhary, J.A. Harding, and M.K. Tiwari Data mining in manufacturing: A review based on the kind of knowledge Journal of Intelligent Manufacturing 20 2009 501 521
10. L. Chunquan Study on assembly quality fault diagnosis system of chip components based on fuzzy analysis WSEAS Transactions on Systems 6 2007 109 116
11. L. Chunquan, Z. Dejian, and W. Zhaohua Study on SMT solder joint quality fuzzy diagnosis technology based on the theory of solder joint shape China Mechanical Engineering 15 2004 1967 1970
12. D.W. Coit, J. Billa, D. Leonard, A.E. Smith, W. Clark, and A. El-Jaroudi Wave solder process control modeling using a neural network approach C.H. Dagli, B.R. Fernandez, J. Ghosh, R.T.S. Kumara, Intelligent engineering systems through artificial neural networks Vol. 4 1994 ASME Press New York 999 1004
13. D.W. Coit, B.T. Jackson, and A.E. Smith Static neural network process models: Considerations and case studies International Journal of Production Research 36 1998 2953 2967
14. D.W. Coit, B.T. Jackson, and A.E. Smith Neural network open loop control system for wave soldering Journal of Electronics Manufacturing 11 2002 95 105
15. I. Edinbarough, R. Balderas, and S. Bose A vision and robot based on-line inspection monitoring system for electronic manufacturing Computers in Industry 56 2005 989 996
16. A.P. Engelbrecht Computational intelligence: An introduction 2nd edition 2007 John Wiley & Sons West Sussex, England
17. S.D. Eppinger, C.D. Huber, and V.H. Pham A methodology for manufacturing process signature analysis Journal of Manufacturing Systems 14 1995 20 34
18. S. Gebus, and K. Leiviskä Knowledge acquisition for decision support systems on an electronic assembly line Expert Systems with Applications 36 2009 93 101
19. M. Gen, and R. Cheng Genetic algorithms & engineering design 1997 John Wiley & Sons United States of America
20. A. Giaquinto, G. Fornarelli, G. Brunetti, and G. Acciani A neurofuzzy method for the evaluation of soldering global quality index IEEE Transactions on Industrial Informatics 5 2009 56 66
21. S.K. Halgamuge, and M. Glesner Neural networks in designing fuzzy systems for real world applications Fuzzy Sets and Systems 65 1994 1 12
22. D. Hand, H. Mannila, and P. Smyth Principles of data mining 2001 MIT Press Cambridge (MA)
23. J.A. Harding, M. Shahbaz, Srinivas, and A. Kusiak Data mining in manufacturing: A review Journal of Manufacturing Science and Engineering 128 2006 969 976
24. Havia, E.; Bernhardt, E.; Mikkonen, T.; Montonen, H.; Alatalo, M. (2005). Implementation of Lead-free Wave Soldering Process. In Proceedings of the electronics production and packaging technology, ELTUPAK 2005.
25. S. Haykin Neural networks and learning machines 3rd ed. 2009 Pearson Education Inc. Upper Saddle River, New Jersey
26. P. Helo Managing agility and productivity in the electronics industry Industrial Management & Data Systems 104 2004 567 577
27. J.H. Holland Adaptation in natural and artificial systems 1975 University of Michigan Press Ann Arbor
28. S. Jagannathan Automatic inspection of wave soldered joints using neural networks Journal of Manufacturing Systems 16 1997 389 398
29. S. Jagannathan, D. Seebaluck, and J.D. Jenness Intelligent inspection of wave-soldered joints Journal of Manufacturing Systems 11 1992 137 143
30. M. Judd, and K. Brindley Soldering in electronics assembly 2nd edition 1999 Newnes Oxford, MA
31. P. Kadlec, B. Gabrys, and S. Strandt Data-driven soft sensors in the process industry Computers and Chemical Engineering 33 2009 795 814
32. R.S. Khandpur Printed circuit boards: Design, fabrication, and assembly 2005 McGraw-Hill United States of America
33. J.H. Kim, and H.S. Cho Neural network-based inspection of solder joints using a circular illumination Image and Vision Computing 13 1995 479 490
34. J.H. Kim, H.S. Cho, and S. Kim Pattern classification of solder joint images using a correlation neural network Engineering Applications of Artificial Intelligence 9 1996 655 669
35. T.-H. Kim, T.-H. Cho, Y.S. Moon, and S.H. Park Visual inspection system for the classification of solder joints Pattern Recognition 32 1999 565 575
36. Kim, T. -H.; Cho, T. -H.; Moon, Y. -S.; Park, S. -H. (1996b). An Automated Visual Inspection of Solder Joints Using 2D and 3D Features. In Proceedings of the 3rd IEEE workshop on applications of computer vision, WACV '96 (pp. 110-115).
37. K.W. Ko, and H.S. Cho Solder joints inspection using a neural network and fuzzy rule-based classification method IEEE Transactions on Electronics Packaging Manufacturing 23 2000 93 103
38. T. Kohonen Self-organizing maps 3rd edition 2001 Springer-Verlag Berlin Heidelberg
39. Y. Koji, N. Takatsu, and M. Oh Visual solder inspection using neural network Systems and Computers in Japan 27 1996 92 100
40. A. Kusiak, and C. Kurasek Data mining of printed circuit board defects IEEE Transactions on Robotics and Automation 17 2001 191 196
41. N.-C. Lee Reflow soldering processes and troubleshooting: SMT, BGA, CSP and flip chip technologies 2002 Newnes USA
42. Y. Li, R.L. Mahajan, and J. Tong Design factors and their effect on PCB assembly yield: Statistical and neural network predictive models IEEE Transactions on Components, Packaging, and Manufacturing Technology - Part A 17 1994 183 191
43. Y.-H. Lin, W.-J. Deng, J.-R. Shie, and Y.-K. Yang Optimization of reflow soldering process for BGA packages by artificial neural network Microelectronics International 24 2007 64 70
44. S. Liu, I.C. Ume, and A. Achari Defects pattern recognition for flip-chip solder joint quality inspection with laser ultrasound and interferometer IEEE Transactions on Electronics Packaging Manufacturing 27 2004 59 66
45. M. Liukkonen, E. Havia, H. Leinonen, and Y. Hiltunen Application of self-organizing maps in analysis of wave soldering process Expert Systems with Applications 36 2009 4604 4609
46. M. Liukkonen, E. Havia, H. Leinonen, and Y. Hiltunen Quality-oriented optimization of wave soldering process by using self-organizing maps Applied Soft Computing 11 2010 214 220
47. M. Liukkonen, T. Hiltunen, E. Havia, H. Leinonen, and Y. Hiltunen Modeling of soldering quality by using artificial neural networks IEEE Transactions on Electronics Packaging Manufacturing 32 2009 89 96
48. S.-L. Lu, X.-M. Zhang, and Y.-C. Kuang Neural network-based inspecting method of PCB solder joint Journal of South China University of Technology (Natural Science) 36 2008 135 139
49. C.O. Malave, and T. Sastri Prediction of wave solder machine parameters based on printed circuit board design characteristics C.H. Dagli, L.I. Burke, Y.C. Shin, Intelligent engineering systems through artificial neural networks Vol. 2 1992 ASME Press New York 833 838
50. M. Matsushima, N. Kawai, H. Fujie, K. Yasuda, and K. Fujimoto Visual inspection of soldering joints by neural network with multi-angle view and principal component analysis Service Robotics and Mechatronics Part 16 2010 329 334
51. M.R.G. Meireles, P.E.M. Almeida, and M.G. Simões A comprehensive review for industrial applicability of artificial neural networks IEEE Transactions on Industrial Electronics 50 2003 585 601
52. Meyer, S.; Wohlrabe, H.; Wolter, K. -J. (2009). Data Mining in Electronics Packaging. In Proceedings of the 32nd international spring seminar on electronics technology (pp. 1-7).
53. C. Neubauer Intelligent X-ray inspection for quality control of solder joints IEEE Transactions on Components, Packaging & Manufacturing Technology - Part C: Manufacturing 20 1997 111 120
54. T.Y. Ong, Z. Samad, and M.M. Ratnam Solder joint inspection with multi-angle imaging and an artificial neural network The International Journal of Advanced Manufacturing Technology 38 2008 455 462
55. N.R. Pal, and S. Pal Editorial: Computational intelligence for pattern recognition International Journal of Pattern Recognition and Artificial Intelligence 16 2002 773 779
56. Z. Pawlak Rough sets International Journal of Computer and Information Sciences 11 1982 341 356
57. B.L. Pierce, D.J. Shelton, H.G. Longbotham, S. Baddipudi, and P. Yan Automated inspection of through hole solder joints utilizing X-ray imaging IEEE Aerospace and Electronics Systems Magazine 9 1994 28 32
58. S. Pupin, and M. Resadi Solder joints inspection machine VISA: Advanced vision inspection system Automazione e Strumentazione 42 1994 107 112
59. J.R. Quinlan Induction of decision trees Machine Learning 1 1986 81 106
60. M. Riedmiller Advanced supervised learning in multi-layer perceptrons: From backpropagation to adaptive learning algorithms Computer Standards and Interfaces 16 1994 265 278
61. Y.J. Roh, W.S. Park, and H. Cho Correcting image distortion in the X-ray digital tomosynthesis system for PCB solder joint inspection Image and Vision Computing 21 2003 1063 1075
62. Y.K. Ryu, and H.S. Cho A neural network approach to extended gaussian image based solder joint inspection Mechatronics 7 1997 159 184
63. A. Saenthon, and S. Kaitwanidvilai Development of new edge-detection filter based on genetic algorithm: An application to solder joint inspection International Journal of Advanced Manufacturing Technology 46 2010 1009 1019
64. V. Sankaran, M.J. Embrechts, L.-E. Harsson, R.P. Kraft, and D.L. Millard Applications of backpropagation in printed wiring board inspection C. Dagli, M. Akay, C.L.P. Chen, B.R. Fernandez, J. Ghosh, Intelligent engineering systems through artificial neural networks, fuzzy logic and evolutionary programming Vol. 5 1995 ASME Press New York 931 936
65. V. Sankaran, A.R. Kalukin, and R.P. Kraft Improvements to X-ray laminography for automated inspection of solder joints IEEE Transactions on Components, Packaging, and Manufacturing Technology - Part C 21 1998 148 154
66. Specht, D. F. (1988). Probabilistic Neural Networks for Classification, Mapping, or Associative Memory. In Proceedings of IEEE international conference on neural networks (pp. 525-532).
67. B.K. Tripathy Rough sets on fuzzy approximation spaces and intuitionistic fuzzy approximation spaces A. Abraham, R. Falcón, R. Bello, Rough set theory: A true landmark in data analysis, studies in computational intelligence Vol. 174 2009 Springer-Verlag Berlin Heidelberg
68. T.-N. Tsai Development of an integrated reflow soldering control system using incremental hybrid process knowledge Expert Systems with Applications 28 2005 681 692
69. T.-N. Tsai, and T. Yang A neuro-computing approach to the thermal profile control of the second-side reflow process in surface mount assembly Journal of Manufacturing Technology Management 16 2005 343 359
70. T.-N. Tsai, T. Yang, and P.-A. Hou Neurofuzzy modeling of the reflow thermal profile for surface mount assembly International Journal of Systems Science 36 2005 89 101
71. T.-L. Tseng, M.C. Jothishankar, and T. Wu Quality control problem in printed circuit board manufacturing - An extended rough set theory approach Journal of Manufacturing Systems 23 2004 56 72
72. L. Tsironis, N. Bilalis, and V. Moustakis Using machine learning to support quality management - Framework and experimental investigation The TQM Magazine 17 2005 237 248
73. J.M. Twomey, and A.E. Smith Artificial neural network approach to the control of a wave soldering process Intelligent Engineering Systems Through Artificial Neural Networks 6 1996 889 894
74. V.N. Vapnik Statistical learning theory 1998 John Wiley & Sons United States of America
75. Voci, F.; Eiho, S.; Sugimoto, N. (2002). Fuzzy Interference Filter and Morphological Operators for Short Circuits Detection in Printed Circuit Board. In Proceedings the international symposium on industrial electronics (pp. 672-677).
76. K. Wang Applying data mining to manufacturing: The nature and implications Journal of Intelligent Manufacturing 18 2007 487 495
77. X. Wei, and T. Frantti Adaptive real-time fuzzy x-ray solder joint inspection system Journal of Manufacturing Systems 21 2002 111 125
78. T. Yang, and T.-N. Tsai Modeling and implementation of a neurofuzzy system for surface mount assembly defect prediction and control IIE Transactions 34 2002 637 646
79. T.S. Yun, K.J. Sim, and H.J. Kim Support vector machine-based inspection of solder joints using circular illumination Electronics Letters 36 2000 949 951
80. L.A. Zadeh Fuzzy sets Information and Control 8 1965 338 353
81. Zhou, J. -C.; Xiao, X. -Q.; En, Y.-F.; Chen, N.; Wang, X. -Z. (2008). Thermo-Mechanical Fatigue Reliability Optimization of PBGA Solder Joints Based on ANN-PSO. Journal of Central South University of Technology, English edition, 15, pp. 689-693.