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

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Abstract

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.

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Authors and Affiliations

  1. Materials and Energy Research Center (MERC), Tehran, Iran

    Mohsen Ostad Shabani, Mohammad Reza Rahimipour & Ali Asghar Tofigh

  2. Department of Materials Engineering, Sharif University of Technology, Tehran, Iran

    Parviz Davami

Authors
  1. Mohsen Ostad Shabani
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  2. Mohammad Reza Rahimipour
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  3. Ali Asghar Tofigh
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  4. Parviz Davami
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Correspondence to Ali Asghar Tofigh.

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Shabani, M.O., Rahimipour, M.R., Tofigh, A.A. et al. Refined microstructure of compo cast nanocomposites: the performance of combined neuro-computing, fuzzy logic and particle swarm techniques. Neural Comput & Applic 26, 899–909 (2015). https://doi.org/10.1007/s00521-014-1724-8

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  • DOI: https://doi.org/10.1007/s00521-014-1724-8

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