A case-based evolutionary model for defect classification of printed circuit board images

In this research, a case-based evolutionary identification model is developed for PCB defect classification problems. Image understanding is the first and foremost step in the inspection of printed circuit boards. This paper presents a two-phase method for the segmentation of printed circuit board (PCB) images. In the first phase, a set of defect images of several existing basic patterns are stored to form a concept space. In the second phase, a new pattern is evolutionally grabbed using some primitive operators generated by calculating the relative position of several similar cases in the concept space. The case-based reasoning system relies on the software agents derived from past experience within the domain database to determine what feature is required to deliver new patterns in satisfying user’s requirements. Experimental results show that the proposed approach is very effective in identifying the defect patterns.

[1]  Jose A. Ventura,et al.  A neural network approach for defect identification and classification on leather fabric , 2000, J. Intell. Manuf..

[2]  Fikret Erçal,et al.  Segmentation of Printed Circuit Board Images into Basic Patterns , 1998, Comput. Vis. Image Underst..

[3]  Yasuhiko Hara,et al.  Automatic Inspection System for Printed Circuit Boards , 1983, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  H. F. Spence.  Printed circuit board diagnosis using artificial neural networks and circuit magnetic fields , 1993, AUTOTESTCON 93.

[5]  David T. Lee.  A Computerized Automatic Inspection System For Complex Printed Thick Film Patterns , 1978, Other Conferences.

[6]  Wen-Yen Wu,et al.  Automated inspection of printed circuit boards through machine vision , 1996 .

[7]  Anil K. Jain,et al.  Machine-vision techniques for inspection of printed wiring boards and thick-film circuits , 1986 .

[8]  Orhan Torkul,et al.  An industrial visual inspection system that uses inductive learning , 2004, J. Intell. Manuf..

[9]  Linda G. Shapiro,et al.  A Structural Model of Shape , 1980, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[10]  Fernando Torres Medina,et al.  Automatic inspection for phase-shift reflection defects in aluminum web production , 2002, J. Intell. Manuf..

[11]  R. J. Richards,et al.  Fast multi-resolution image processing for PCB manufacture , 1995 .

[12]  William J Buchanan,et al.  Excluding inclusions [PCB inspection] , 1999 .

[13]  J. Lopez,et al.  An artificial neural network printed circuit board diagnostic system based on infrared energy emissions , 1991, Conference Record AUTOTESTCON '91 IEEE Systems Readiness Technology Conference Improving Systems Effectiveness in the Changing Environment of the '90s.

[14]  J. D. T. Tannock,et al.  A neural network approach to characterize pattern parameters in process control charts , 1999, J. Intell. Manuf..

[15]  E. K. Lo,et al.  A review of inspection techniques applicable to PCB manufacturing and assembly, particularly with respect to SMT , 1991 .

[16]  Charles A. Harlow,et al.  Automatic Visual Inspection Of Printed Circuit Boards , 1979, Optics & Photonics.

[17]  D. S. Reay,et al.  The influence of computational developments on control implementation―a case study in robotics , 1991 .

[18]  Cihan H. Dagli,et al.  Automatic PCB Inspection Algorithms: A Survey , 1996, Comput. Vis. Image Underst..

[19]  Fikret Erçal,et al.  A Subpattern Level Inspection System for Printed Circuit Boards , 1998, Comput. Vis. Image Underst..

[20]  Anil K. Jain,et al.  A Rule Based Approach for Visual Pattern Inspection , 1988, IEEE Trans. Pattern Anal. Mach. Intell..

[21]  Mineo Nomoto,et al.  Automated pattern inspection system for PCB photomasks using design pattern comparison method , 1990, [Proceedings] IECON '90: 16th Annual Conference of IEEE Industrial Electronics Society.