Optimal laser-cutting parameters for QFN packages by utilizing artificial neural networks and genetic algorithm

2008 ◽  
Vol 208 (1-3) ◽  
pp. 270-283 ◽  
Author(s):  
Ming-Jong Tsai ◽  
Chen-Hao Li ◽  
Cheng-Che Chen
Keyword(s):  
Genetic Algorithm ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Laser Cutting ◽  
Cutting Parameters ◽  
Artificial Neural

scholarly journals Cutting Insert and Parameter Optimization for Turning Based on Artificial Neural Networks and a Genetic Algorithm

2019 ◽  
Vol 9 (3) ◽  
pp. 479 ◽  
Author(s):  
Bolivar Solarte-Pardo ◽  
Diego Hidalgo ◽  
Syh-Shiuh Yeh
Keyword(s):  
Genetic Algorithm ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Technical Information ◽  
Cutting Parameters ◽  
Machining Performance ◽  
Machining Time ◽  
Cutting Velocity ◽  
Cutting Insert ◽  
Artificial Neural

The objective of this present study is to develop a system to optimize cutting insert selection and cutting parameters. The proposed approach addresses turning processes that use technical information from a tool supplier. The proposed system is based on artificial neural networks and a genetic algorithm, which define the modeling and optimization stages, respectively. For the modeling stage, two artificial neural networks are implemented to evaluate the feed rate and cutting velocity parameters. These models are defined as functions of insert features and working conditions. For the optimization problem, a genetic algorithm is implemented to search an optimal tool insert. This heuristic algorithm is evaluated using a custom objective function, which assesses the machining performance based on the given working specifications, such as the lowest power consumption, the shortest machining time or an acceptable surface roughness.

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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