Topology Optimisation Using MPBILs and Multi-Grid Ground Element

Sleesongsom, Suwin; Bureerat, Sujin

doi:10.3390/app8020271

Cited by 14 publications

(10 citation statements)

References 28 publications

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“…The means of the objective function values obtained from the various MHs are used as their performance indicator. Also, the comparison based on nonparametric statistical Friedman test [ 17 , 31 ] is employed. Thus, this study would be a proper baseline for the topic of using MHs for four-bar linkage path synthesis in the future.…”

Section: Numerical Experimentsmentioning

confidence: 99%

Optimal Synthesis of Four-Bar Linkage Path Generation through Evolutionary Computation with a Novel Constraint Handling Technique

Sleesongsom

Bureerat

2018

Computational Intelligence and Neuroscience

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This paper presents a novel constraint handling technique for optimum path generation of four-bar linkages using evolutionary algorithms (EAs). Usually, the design problem is assigned to minimize the error between desired and obtained coupler curves with penalty constraints. It is found that the currently used constraint handling technique is rather inefficient. In this work, we propose a new technique, termed a path repairing technique, to deal with the constraints for both input crank rotation and Grashof criterion. Three traditional path generation test problems are used to test the proposed technique. Metaheuristic algorithms, namely, artificial bee colony optimization (ABC), adaptive differential evolution with optional external archive (JADE), population-based incremental learning (PBIL), teaching-learning-based optimization (TLBO), real-code ant colony optimization (ACOR), a grey wolf optimizer (GWO), and a sine cosine algorithm (SCA), are applied for finding the optimum solutions. The results show that new technique is a superior constraint handling technique while TLBO is the best method for synthesizing four-bar linkages.

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Section: Numerical Experimentsmentioning

confidence: 99%

Optimal Synthesis of Four-Bar Linkage Path Generation through Evolutionary Computation with a Novel Constraint Handling Technique

Sleesongsom

Bureerat

2018

Computational Intelligence and Neuroscience

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“…The quasi-optimal solution is not the best solution, but it is close to the global optimal solution of the problem [1]. In this regard, optimization algorithms have been applied by scientists in various fields such as energy [4][5][6], protection [7], electrical engineering [8][9][10][11][12][13], topology optimization [14] and energy carriers [15][16][17] to achieve the quasi-optimal solution. Table 1 shows the optimization algorithms grouped according to the main design idea.…”

Section: Introductionmentioning

confidence: 99%

DM: Dehghani Method for Modifying Optimization Algorithms

et al. 2020

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In recent decades, many optimization algorithms have been proposed by researchers to solve optimization problems in various branches of science. Optimization algorithms are designed based on various phenomena in nature, the laws of physics, the rules of individual and group games, the behaviors of animals, plants and other living things. Implementation of optimization algorithms on some objective functions has been successful and in others has led to failure. Improving the optimization process and adding modification phases to the optimization algorithms can lead to more acceptable and appropriate solution. In this paper, a new method called Dehghani method (DM) is introduced to improve optimization algorithms. DM effects on the location of the best member of the population using information of population location. In fact, DM shows that all members of a population, even the worst one, can contribute to the development of the population. DM has been mathematically modeled and its effect has been investigated on several optimization algorithms including: genetic algorithm (GA), particle swarm optimization (PSO), gravitational search algorithm (GSA), teaching-learning-based optimization (TLBO), and grey wolf optimizer (GWO). In order to evaluate the ability of the proposed method to improve the performance of optimization algorithms, the mentioned algorithms have been implemented in both version of original and improved by DM on a set of twenty-three standard objective functions. The simulation results show that the modified optimization algorithms with DM provide more acceptable and competitive performance than the original versions in solving optimization problems.

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“…In other applications, Sleesongsom and Bureerat [16] apply a topology optimization method using multi-objective population-based incremental learning and multigrid ground element. It is found that the proposed optimization method shows superiority over other techniques.…”

Section: Introductionmentioning

confidence: 99%

Topology and Response Surface Optimization of a Bicycle Crank Arm with Multiple Load Cases

Ismail

Koo

2020

Applied Sciences

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This paper presents an application of topology optimization and response surface method to optimize the geometry of a bicycle crank arm and the experimental validation of it. This is purposely to reduce the crank arm mass and create a preliminary design of a lightweight structure necessary for the high-performance bicycle development. A three-dimensional bike crank arm model was made in the SpaceClaim software followed by a static finite element analysis using ANSYS Workbench 2019 R1. A multiple cycling load was applied simultaneously in seven crank angles of 30, 45, 60, 90, 120, 135, and 150° relative to the horizontal position to create the multiple loads to the crank. From there, topology optimization was then conducted to investigate the effect of mass constraint, stress constraint, angle of cycling, and crank materials on the topological pattern result. To minimize stress concentration at corners, a shape optimization using the response surface method was conducted and obtained the final geometry. From the result, it is shown that both optimization methods not only successfully reduce the crank arm mass and provide several optimum design options but also are able to reduce the maximum stress in the crank arm up to 20% after the optimization process. The experimental validation using a newly developed wireless measurement system shows a considerable agreement to the numerical results.

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Topology Optimisation Using MPBILs and Multi-Grid Ground Element

Cited by 14 publications

References 28 publications

Optimal Synthesis of Four-Bar Linkage Path Generation through Evolutionary Computation with a Novel Constraint Handling Technique

Optimal Synthesis of Four-Bar Linkage Path Generation through Evolutionary Computation with a Novel Constraint Handling Technique

DM: Dehghani Method for Modifying Optimization Algorithms

Topology and Response Surface Optimization of a Bicycle Crank Arm with Multiple Load Cases

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