Detecting resonances in conservative maps using evolutionary algorithms

International Journal of Damage Mechanics

Elrefaei

2014

In this present work, stir casting technique was used to fabricate aluminum matrix composites with varying weight percentages of SiC (5, 10 and 15) reinforcements. The proper selection of process parameter such as pouring temperature, stirring speed, stirring time and pre-heat temperature of reinforcement can all influence the quality of the fabricated composites. The porosity level of composite should be minimized and the chemical reaction between reinforcement and matrix should be avoided. Optimization is an applied science which explores the best values of the parameters of a problem that may take under specified conditions. The execution of an optimized stir casting technique yields relatively homogenous and fine microstructure which improves the addition of reinforcement material in the molten metal. The influence of SiC content, SiC size and secondary mechanical processing with different rolling reductions on the dry sliding wear characteristics of Al matrix composites has been assessed using a pin-on-disc wear test. The porosity and hardness of the resultant composites were also examined.

Section: Genetic Algorithmmentioning

confidence: 99%

Searching for the superior solution to the population-based optimization problem: Processing of the wear resistant commercial AA6061 AMCs

Mazahery

International Journal of Damage Mechanics

Elrefaei

2014

“…The individuals in a PSO have a position and a velocity and are denoted as particles. 48–54 The PSO traditionally has no crossover between individuals, has no mutation and particles are never substituted by other individuals during the run. The PSO algorithm works by attracting the particles to search space positions of high fitness.…”

Section: Modelingmentioning

confidence: 99%

Extruded AA6061 alloy matrix composites: The performance of multi-strategies to extend the searching area of the optimization algorithm

Mazahery

Journal of Composite Materials

2013

This article focuses on the development of a comprehensive method for optimization of casting parameters including stirring time and speed of stirrer. Particulate AA6061 aluminum alloy matrix composites were produced by compocasting. First, the effects of extrusion and particles coating on the microstructures and mechanical properties of the composites were investigated. Then, a hybrid algorithm based on PSO and GA was implemented in order to solve the global problems and optimize the coefficients of equations with much better performance, resulting in higher possibility for industry application. PSO evolved the population over a certain number of generations, retained the best M particles and excluded the remaining pop size-M particles. Selection, crossover and mutation GA operators generated pop size M new individuals and combined them with the best M particles to form a new population for the next generation. The model based on combination of GA and PSO is capable to predict with greater reliability than single optimization methods including ACO and multiple linear regression.

“…34 Particle swarm optimization (PSO) is a modern evolutionary computation technique based on a population mechanism. 35 It has been motivated by the simulation of the social behavior of individuals (particles). [36][37][38][39][40][41][42] Since Kennedy 43 invented the PSO, the challenge has been to employ the algorithm to different problem areas other than those that the inventors originally focused on.…”

Section: Introductionmentioning

confidence: 99%

Application of the combined neuro-computing, fuzzy logic and swarm intelligence for optimization of compocast nanocomposites

Tofigh

Rahimipour

Journal of Composite Materials

et al. 2014

In the last few years, an increasing attention has been paid to the issues of saving energy and reducing the manufacturing costs in the transport industry which necessitates further efforts to replace traditional materials like steel with lightweight materials such as plastics, aluminum, magnesium, and composites. Metal matrix nanocomposites have turned into an established material in today’s industry with an ongoing expansion in their field of applications. In this study, the formation of nanoparticle–aluminum metal matrix composites is described by compocast processing from nanoparticle Al2O3 and the A356 aluminum alloy. In order to optimize the processing parameters, a novel approach is implemented which relies on combination of adaptive neuro-fuzzy inference system and particle swarm optimization method.