Journal ArticleDOI
Development of the principle of simulated natural evolution in searching for a more superior solution: Proper selection of processing parameters in AMCs
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In this article, an optimized stir casting technique was used to fabricate aluminum matrix composites with varying weight percentages of SiC (5, 10, and 15) reinforcements, which yields relatively homogenous and fine microstructure which improves the addition of reinforcement material in the molten metal.About:
This article is published in Powder Technology.The article was published on 2013-09-01. It has received 36 citations till now. The article focuses on the topics: Process variable.read more
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Investigations on mechanical and machinability behavior of aluminum/flyash cenosphere/Gr hybrid composites processed through compocasting
Soorya Prakash Kumarasamy,Kavimani Vijayananth,Titus Thankachan,Gopal Pudhupalayam Muthukutti +3 more
TL;DR: In this paper, a hybrid aluminum metal matrix composite (AMMC) was developed through two-step compocasting method by reinforcing constant amount of flyash cenosphere (10%) and varying quantity of graphite (2, 4% and 6%).
Journal ArticleDOI
Optimization of wear parameters and their relative effects on stir cast AA6063-Si3N4 Composite
TL;DR: In this paper, the minimum wear rate was obtained for the composite containing 10 wt% Si3N4, load of 29.43 N, sliding distance of 1500 m and the sliding velocity of 3 m s−1.
Journal ArticleDOI
Application of the combined neuro-computing, fuzzy logic and swarm intelligence for optimization of compocast nanocomposites
TL;DR: In this article, the formation of nanoparticle-aluminum metal matrix composites is described by compocast processing from nanoparticle Al2O3 and the A356 aluminum alloy, a novel approach is implemented which relies on combination of adaptive neuro-fuzzy inference system and particle swarm optimization method.
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Refined microstructure of compo cast nanocomposites: the performance of combined neuro-computing, fuzzy logic and particle swarm techniques
TL;DR: Adaptive neuro-fuzzy inference system combined with particle swarm optimization method is implemented in this research study in order to optimize the parameters in processing of aluminum MMCs.
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Strengthening mechanisms and modelling of mechanical properties of submicron-TiB2 particulate reinforced Al 7075 metal matrix composites
TL;DR: In this article, an analytical model was proposed to predict elastic modulus and yield strength of the MMCs considering the effects of porosity, and the proposed models were validated with the experimental results and predicted results of some established models.
References
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Journal ArticleDOI
Comprehensive learning particle swarm optimizer for global optimization of multimodal functions
TL;DR: The comprehensive learning particle swarm optimizer (CLPSO) is presented, which uses a novel learning strategy whereby all other particles' historical best information is used to update a particle's velocity.
Proceedings ArticleDOI
Population structure and particle swarm performance
James Kennedy,Rui Mendes +1 more
TL;DR: The effects of various population topologies on the particle swarm algorithm were systematically investigated and it was discovered that previous assumptions may not have been correct.
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A study of particle swarm optimization particle trajectories
TL;DR: Current theoretical studies on particle swarm optimization are extended to investigate particle trajectories for general swarms to include the influence of the inertia term, and a formal proof that each particle converges to a stable point is provided.
Proceedings ArticleDOI
Small worlds and mega-minds: effects of neighborhood topology on particle swarm performance
TL;DR: The study manipulated the neighborhood topologies of particle swarms optimizing four test functions and Sociometric structure and the small-world manipulation interacted with function to produce a significant effect on performance.
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A sequential niche technique for multimodal function optimization
TL;DR: An algorithm based on a traditional genetic algorithm that involves iterating the GA but uses knowledge gained during one iteration to avoid re-searching, on subsequent iterations, regions of problem space where solutions have already been found.