Multidisciplinary Heat Generating Cell Placement Optimization Using Genetic Algorithm and Artificial Neural Networks

2006 ◽  
Author(s):  
Tohru Suwa ◽  
Hamid Hadim
Keyword(s):  
Genetic Algorithm ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Placement Optimization ◽  
Artificial Neural ◽  
Cell Placement

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.

MATHEMATICAL SUPPORT FOR INDUCTION SOLDERING CONTROL BASED ON INTELLIGENT METHODS OF INFORMATION PROCESSING

2021 ◽  
pp. 81-87
Author(s):  
А.В. Милов
Keyword(s):  
Genetic Algorithm ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Information Processing ◽  
Mathematical Models ◽  
Technological Process ◽  
Artificial Neural ◽  
Intelligent Methods

В статье представлены математические модели на основе искусственных нейронных сетей, используемые для управления индукционной пайкой. Обучение искусственных нейронных сетей производилось с использованием многокритериального генетического алгоритма FFGA. This article presents mathematical models based on artificial neural networks used to control induction soldering. The artificial neural networks were trained using the FFGA multicriteria genetic algorithm. The developed models allow to control induction soldering under conditions of incomplete or unreliable information, as well as under conditions of complete absence of information about the technological process.

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