Printed circuit board diagnosis using artificial neural networks and circuit magnetic fields

1994 ◽  
Vol 9 (2) ◽  
pp. 20-24 ◽  
Author(s):  
H.F. Spence
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Magnetic Fields ◽  
Printed Circuit Board ◽  
Circuit Board ◽  
Printed Circuit ◽  
Artificial Neural

Inspection System of Soldering Joint on Printed Circuit Board by Using Neural Network

1995 ◽  
Vol 7 (3) ◽  
pp. 225-229
Author(s):  
Shunichiro Oe ◽  
◽  
Kennichi Kaida ◽  
Daisuke Nagai ◽  
Mituo Nakamura ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Semiconductor Laser ◽  
Printed Circuit Board ◽  
The State ◽  
Circuit Board ◽  
Inspection System ◽  
Sensor Unit ◽  
Printed Circuit ◽  
The Neural Networks

This paper deals with a new inspection system of soldering joint on printed circuit board by using neural network. A sensor unit of this system consists of a semiconductor laser unit, four PSDs, and a pin photo-diode. We can obtain four types of images which are called height image, PSD brightness image, vertical image and vector image, by using four sensor units. We extract the features which show the state of soldering joint from these images and develop an inspection system using the neural networks constructed for the features and the state of soldering joint.

Multidisciplinary Placement Optimization of Heat Generating Electronic Components on Printed Circuit Boards Using Artificial Neural Networks

2005 ◽  
Author(s):  
Tohru Suwa ◽  
Hamid A. Hadim
Keyword(s):  
Genetic Algorithm ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Printed Circuit Boards ◽  
Circuit Boards ◽  
Electronic Components ◽  
Printed Circuit ◽  
Placement Optimization ◽  
Artificial Neural ◽  
Optimization Methodology

A multidisciplinary placement optimization methodology for heat generating electronic components on printed circuit boards (PCBs) is presented. The methodology includes thermal, electrical and placement criteria involving junction temperature, wiring density, line length for high frequency signals, and critical component location which are optimized simultaneously using the genetic algorithm. A board-level thermal performance prediction methodology which is based on a combination of a superposition method and artificial neural networks (ANNs) is developed for this study. Two genetic algorithms with different thermal prediction methods are used in a cascade in the optimization process. The first genetic algorithm is based on simplified thermal network modeling and it is mainly aimed at finding component locations that avoid any overlap. Compact thermal models are used in the second genetic algorithm leading to more accurate thermal prediction which improves the placement optimization obtained using the first algorithm. Using this optimization methodology, large calculation time reduction is achieved without losing accuracy. To demonstrate the capabilities of the present methodology, a test case involving component placement on a PCB is presented.

Estimating printed circuit board assembly times using neural networks

2009 ◽  
Vol 48 (8) ◽  
pp. 2201-2218 ◽  
Author(s):  
Frans Vainio ◽  
Michael Maier ◽  
Timo Knuutila ◽  
Esa Alhoniemi ◽  
Mika Johnsson ◽  
...  
Keyword(s):  
Neural Networks ◽  
Printed Circuit Board ◽  
Circuit Board ◽  
Printed Circuit Board Assembly ◽  
Printed Circuit ◽  
Circuit Board Assembly
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