scholarly journals Optimal Dummy Pattern Design Method for PWB Warpage Control Using the Human-Based Genetic Algorithm

Micromachines ◽  
2020 ◽  
Vol 11 (9) ◽  
pp. 807
Author(s):  
Sun Kyoung Kim ◽  
Sang-Hyuk Lee
Keyword(s):  
Genetic Algorithm ◽  
Design Method ◽  
Optimization Method ◽  
Simulation Tool ◽  
Printed Wiring Board ◽  
Pattern Design ◽  
Input And Output ◽  
Direct Modeling ◽  
Scripting Language ◽  
Wiring Board

In this work, a method that minimizes printed wiring board (PWB) warpage by dummy pattern design is proposed. This work suggests that dummy patterns are placed on a preset discretized location in the PWB to reduce the warpage. On each discretized candidate area, the dummy pattern can be set or unset. The warpage is numerically simulated based on direct modeling of the as-is PWB patterns to evaluate the warpage alongside the dummy pattern design set. The optimal pattern that minimizes warpage is determined using the human-based genetic algorithm where the objective function is evaluated by the structural simulation. The optimization method is realized in a spreadsheet that allows scripting language with which the input and output files of the simulation tool can be modified and read. Two different cases have been tested and the results show that the method can determine the optimal dummy patterns. The measured and simulated deflections agree well with each other. Moreover, it has been shown that certain dummy pattern designs that should reduce the warpage can be sought by the optimization.

Research on the Facilities Layout Planning in MTO Manufacturing Industry Based on Genetic Algorithm

2013 ◽  
Vol 842 ◽  
pp. 695-702
Author(s):  
Ying Wang ◽  
You Rong Li ◽  
Yu Qiong Zhou
Keyword(s):  
Genetic Algorithm ◽  
Manufacturing Industry ◽  
Design Method ◽  
Facility Layout ◽  
Optimization Method ◽  
Market Demand ◽  
Layout Planning ◽  
Manufacturing Enterprises ◽  
Make To Order ◽  
Systematic Layout Planning

To enlarge production to meet the market demand, its nessasery to improve the present facility layout for MTO (Make-To-Order) manufacturing enterprises. This paper tries to design a optimization method based on genetic algorithm for the facility layout of MTO enterprises. Firstly, SLP (systematic layout planning) was applied to analyze the material and non-material flow interrelation of the workshop. Secondly, a relatively optimum layout was determined after using fuzzy hierarchy estimation to evaluate the schemes. Then the scheme was optimized with genetic algorithm. The result shows that the optimized logistics transport load is obviously less than before. This design method based on genetic algorithm (GA) is proved feasible and effective in the optimization of facility layout.

Optimization of Wind Turbine Blades Using Lifting Surface Method and Genetic Algorithm

2011 ◽  
Author(s):  
Xin Shen ◽  
Xiao-cheng Zhu ◽  
Zhao-hui Du
Keyword(s):  
Genetic Algorithm ◽  
Design Method ◽  
Turbine Blades ◽  
Optimization Method ◽  
Efficient Design ◽  
Wind Turbine Blades ◽  
Surface Method ◽  
Lifting Surface ◽  
Horizontal Axis Wind Turbines ◽  
Wake Model

This paper describes an optimization method for the design of horizontal axis wind turbines using the lifting surface method as the performance prediction model and a genetic algorithm for optimization. The aerodynamic code for the design method is based on the lifting surface method with a prescribed wake model for the description of the wake. A micro genetic algorithm handles the decision variables of the optimization problem such as the chord and twist distribution of the blade. The scope of the optimization method is to achieve the best trade off of the following objectives: maximum of annual energy production and minimum of blade loads including thrust and blade rood flap-wise moment. To illustrate how the optimization of the blade is carried out the procedure is applied to NREL Phase VI rotor. The result shows the optimization model can provide a more efficient design.

Optimization Design of Cantilever Supporting Structure

2013 ◽  
Vol 357-360 ◽  
pp. 2410-2413
Author(s):  
Wei Xu ◽  
Jian Sheng Feng ◽  
Fei Fei Feng
Keyword(s):  
Genetic Algorithm ◽  
Optimization Design ◽  
Design Method ◽  
Optimal Solution ◽  
Optimization Method ◽  
Original Design ◽  
Supporting Structure ◽  
Primary Object ◽  
Design Variables ◽  
Optimum Solution

The primary object of this fundamental research is to reveal the application of genetic algorithm improved on the optimization design of cantilever supporting structure. In order to meet the strength of pile body and pile top displacement as well as design variables subjected to constraint, an algorithm is carried on to seek the optimum solution and relevant examples by means of comprehensively considering the effects on center-to-center spacing between piles,pile diameter and quantity of distributed steel, which is taken the lowest engineering cost as objective function. Through the comparison of the optimized scheme and original design, this fruitful work provides explanation to the effectiveness of genetic algorithm in optimization design. These findings of the research lead to the conclusion that the shortcomings of traditional design method is easy to fall into local optimal solution. The new optimization method can overcome this drawback.

Multi-Objective Aerodynamic Optimization of Axial Turbine Blades Using a Novel Multilevel Genetic Algorithm

2010 ◽  
Vol 132 (4) ◽  
Author(s):  
Özhan Öksüz ◽  
İbrahim Sinan Akmandor
Keyword(s):  
Genetic Algorithm ◽  
Gas Turbine ◽  
Design Method ◽  
Turbine Blades ◽  
Optimization Method ◽  
Navier Stokes ◽  
Test Cases ◽  
Aerodynamic Optimization ◽  
Multi Objective ◽  
Design Variables

In this paper, a new multiploid genetic optimization method handling surrogate models of the CFD solutions is presented and applied for a multi-objective turbine blade aerodynamic optimization problem. A fast, efficient, robust, and automated design method is developed to aerodynamically optimize 3D gas turbine blades. The design objectives are selected as maximizing the adiabatic efficiency and torque so as to reduce the weight, size, and cost of the gas turbine engine. A 3D steady Reynolds averaged Navier–Stokes solver is coupled with an automated unstructured grid generation tool. The solver is verified using two well-known test cases. The blade geometry is modeled by 36 design variables plus the number of blade variables in a row. Fine and coarse grid solutions are respected as high- and low-fidelity models, respectively. One of the test cases is selected as the baseline and is modified by the design process. It was found that the multiploid multi-objective genetic algorithm successfully accelerates the optimization and prevents the convergence with local optimums.

scholarly journals An Investigation of PWB Layout by Genetic Algorithms to Maximize Fatigue Life

1999 ◽  
Vol 121 (1) ◽  
pp. 31-36 ◽  
Author(s):  
A. J. Scholand ◽  
R. E. Fulton ◽  
B. Bras
Keyword(s):  
Genetic Algorithm ◽  
Finite Element Method ◽  
Finite Element ◽  
Genetic Algorithms ◽  
Layout Design ◽  
Design Tool ◽  
Printed Wiring Board ◽  
Computationally Expensive ◽  
Wiring Board

Thermal considerations in printed wiring board (PWB) assemblies are becoming increasingly important as packaging constraints shrink and power use escalates. In this paper, we provide a study on the potential for a genetic algorithm-driven PWB layout design tool to improve the thermal performance of such assemblies. As a case study, the thermomechanical fatigue of surface mounted leadless chip carriers on an FR4 epoxy board is used. We have found that by utilizing appropriate formula-based engineering approximations, the efficiency of parallel implementations of genetic algorithms in finding near-optimal and results makes this approach effective as an explorative “scouting” approach to identify promising board configurations for more computationally expensive evaluations such as finite element method.

Multi Objective Aerodynamic Optimization of Axial Turbine Blades Using a Novel Multi-Level Genetic Algorithm

2008 ◽  
Author(s):  
O¨zhan O¨ksu¨z ◽  
I˙brahim Sinan Akmandor
Keyword(s):  
Genetic Algorithm ◽  
Gas Turbine ◽  
Design Method ◽  
Turbine Blades ◽  
Optimization Method ◽  
Navier Stokes ◽  
Test Cases ◽  
Aerodynamic Optimization ◽  
Multi Objective ◽  
Design Variables

In this paper, a new multiploid genetic optimization method handling surrogate models of the CFD solutions is presented and applied for multi objective turbine blade aerodynamic optimization problem. A fast, efficient, robust, and automated design method is developed to aerodynamically optimize 3D gas turbine blades. The design objectives are selected as maximizing the adiabatic efficiency and torque so as to reduce the weight, size and cost of the gas turbine engine. A 3-Dimensional steady Reynolds Averaged Navier Stokes solver is coupled with an automated unstructured grid generation tool. The solver is verified using two well known test cases. Blade geometry is modeled by 36 design variables plus the number of blades variable in a row. Fine and coarse grid solutions are respected as high and low fidelity models, respectively. One of the test cases is selected as the baseline and is modified by the design process. It was found that the multiploid multi-objective genetic algorithm successfully accelerates the optimization, and prevents converging to local optimums.

scholarly journals A Comprehensive Optimization Design Method of Aerodynamic, Acoustic, and Stealth of Helicopter Rotor Blades Based on Genetic Algorithm

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Mingxu Yi ◽  
Yalin Pan ◽  
Jun Huang ◽  
Lifeng Wang ◽  
Dawei Liu
Keyword(s):  
Genetic Algorithm ◽  
Optimization Design ◽  
Design Method ◽  
Integrated Design ◽  
Optimization Method ◽  
Rotor Blades ◽  
Helicopter Rotor ◽  
Optimization Approach ◽  
Design Variables ◽  
Helicopter Rotor Blades

In this paper, a comprehensive optimization approach is presented to analyze the aerodynamic, acoustic, and stealth characteristics of helicopter rotor blades in hover flight based on the genetic algorithm (GA). The aerodynamic characteristics are simulated by the blade element momentum theory. And the acoustics are computed by the Farassat theory. The stealth performances are calculated through the combination of physical optics (PO) and equivalent currents (MEC). Furthermore, an advanced geometry representation algorithm which applies the class function/shape function transformation (CST) is introduced to generate the airfoil coordinates. This method is utilized to discuss the airfoil shape in terms of server design variables. The aerodynamic, acoustic, and stealth integrated design aims to achieve the minimum radar cross section (RCS) under the constraint of aerodynamic and acoustic requirement through the adjustment of airfoil shape design variables. Two types of rotor are used to illustrate the optimization method. The results obtained in this work show that the proposed technique is effective and acceptable.

Axial Turbine Blade Aerodynamic Optimization Using a Novel Multi-Level Genetic Algorithm

2008 ◽  
Author(s):  
O¨zhan O¨ksu¨z ◽  
I˙brahim Sinan Akmandor
Keyword(s):  
Genetic Algorithm ◽  
Gas Turbine ◽  
Turbine Blade ◽  
Optimization Problems ◽  
Design Method ◽  
Turbine Blades ◽  
Optimization Method ◽  
Test Cases ◽  
Aerodynamic Optimization ◽  
Design Variables

In this paper, a new multiploid genetic optimization method handling surrogate models of the CFD solutions is presented and applied for single objective turbine blade aerodynamic optimization problem. A fast, efficient, robust, and automated design method is developed to aerodynamically optimize 3D gas turbine blades. The design objectives are selected as maximizing the adiabatic efficiency and torque so as to reduce the weight, size and cost of the gas turbine engine. A 3-Dimensional steady Reynolds Averaged Navier Stokes solver is coupled with an automated unstructured grid generation tool. The solver is verified using two well known test cases. Blade geometry is modeled by 36 design variables plus the number of blades variable in a row. Fine and coarse grid solutions are respected as high and low fidelity models, respectively. One of the test cases is selected as the baseline and is modified by the design process. It was found that the multiploid genetic algorithm successfully accelerates the optimization at the initial generations for both optimization problems, while preventing converging to local optimums.

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