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Refined microstructure of compo cast nanocomposites: the performance of combined neuro-computing, fuzzy logic and particle swarm techniques

Ostad Shabani, M ; Sharif University of Technology | 2015

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  1. Type of Document: Article
  2. DOI: 10.1007/s00521-014-1724-8
  3. Publisher: Springer-Verlag London Ltd , 2015
  4. Abstract:
  5. Aluminum metal matrix composites (MMCs) reinforced with nanoceramics are ideal materials for the manufacture of lightweight automotive and other commercial parts. 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. In order to solve the problems associated with poor wettability, agglomeration and gravity segregation of nanoparticles in the melt, a mixture of alumina and aluminum particles was used as the reinforcement instead of raw nanoalumina. Microstructural characterization shows dendritic microstructure for the sand cast and non-dendritic microstructure for the compo cast samples. The fine equiaxed structure of compo cast nanocomposite arises from copious nucleation triggered by a pressure
  6. Keywords:
  7. Alumina ; Aluminum ; Aluminum powder metallurgy ; Computation theory ; Fuzzy inference ; Fuzzy logic ; Fuzzy systems ; Metallic matrix composites ; Microstructure ; Nanocomposites ; Reinforcement ; Adaptive neuro-fuzzy inference system ; Aluminum metal matrix composites ; Micro-structural characterization ; Nano ; Non-dendritic microstructure ; Optimum conditions ; Particle swarm optimization method ; PSO ; Particle swarm optimization (PSO)
  8. Source: Neural Computing and Applications ; Volume 26, Issue 4 , May , 2015 , Pages 899-909 ; 09410643 (ISSN)
  9. URL: http://link.springer.com/article/10.1007%2Fs00521-014-1724-8