Automated visual inspection of bare printed circuit boards using parallel processor hardware

1987 ◽  
Vol 2 (2) ◽  
pp. 69-74 ◽  
Author(s):  
J. W. Foster ◽  
P. Griffin ◽  
J. D. Korry
Keyword(s):  
Visual Inspection ◽  
Printed Circuit Boards ◽  
Circuit Boards ◽  
Parallel Processor ◽  
Automated Visual Inspection ◽  
Printed Circuit

Automated visual inspection of bare printed circuit boards

1990 ◽  
Vol 18 (4) ◽  
pp. 505-509 ◽  
Author(s):  
Paul M. Griffin ◽  
J.René Villalobos ◽  
Joseph W. Foster ◽  
Sherri L. Messimer
Keyword(s):  
Visual Inspection ◽  
Printed Circuit Boards ◽  
Circuit Boards ◽  
Automated Visual Inspection ◽  
Printed Circuit

scholarly journals Solder Paste Scooping Detection by Multilevel Visual Inspection of Printed Circuit Boards

2013 ◽  
Vol 60 (6) ◽  
pp. 2318-2331 ◽  
Author(s):  
Csaba Benedek ◽  
Olivér Krammer ◽  
Mihály Janoczki ◽  
László Jakab
Keyword(s):  
Visual Inspection ◽  
Printed Circuit Boards ◽  
Solder Paste ◽  
Circuit Boards ◽  
Printed Circuit

Visual Inspection of Circuit Boards: Effect of Gender, Age, Defect type, and Defect Proportion

1998 ◽  
Vol 42 (16) ◽  
pp. 1161-1164
Author(s):  
J. Klamklay ◽  
R.R. Bishu
Keyword(s):  
Visual Inspection ◽  
Printed Circuit Boards ◽  
Basic Program ◽  
Inspection Time ◽  
Circuit Boards ◽  
Defect Type ◽  
Printed Circuit ◽  
Independent Variables ◽  
And Gender ◽  
Inspection Performance

Visual inspection of printed circuit boards was evaluated through a simulated experiment. Photographs of circuit boards were scanned into a PC and the images were manipulated using a Visual Basic program. The visual inspection performance measured reaction time in seconds and accuracy by measuring the number of incorrect responses given. The independent variables studied in this research were age, gender, defect type, board size, inspection pace, and proportion of defects. Forty subjects participated in this experiment. In summary, the study showed that defect proportion, defect type, age and gender do influence both inspection time and inspection accuracy. Perhaps the most interesting result is that rate of false alarm (decision 2) and misses (decision 1) depend on defect type and proportion. This has interesting ramification for quality professionals.

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