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

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.

[1]  K. Önel,et al.  Ductility and strength of extruded SiCp/aluminium-alloy composites , 2002 .

[2]  Mohsen Ostad Shabani,et al.  Application of Finite Element Model and Artificial Neural Network in Characterization of Al Matrix Nanocomposites Using Various Training Algorithms , 2012, Metallurgical and Materials Transactions A.

[3]  Brian Cantor,et al.  Characterization of Reinforcement Distribution in Cast Al-Alloy/SiCp Composites , 1998 .

[4]  Sanghamitra Bandyopadhyay,et al.  Multi-Objective Particle Swarm Optimization with time variant inertia and acceleration coefficients , 2007, Inf. Sci..

[5]  Claudia Marcela Méndez,et al.  Corrosion Resistance of Directionally Solidified Casting Zinc-Aluminum Matrix , 2012 .

[6]  Mohsen Ostad Shabani,et al.  Assistance of Novel Artificial Intelligence in Optimization of Aluminum Matrix Nanocomposite by Genetic Algorithm , 2012, Metallurgical and Materials Transactions A.

[7]  Ching-Yuen Chan,et al.  Chaotic Hybrid Algorithm and Its Application in Circle Detection , 2010, EvoApplications.

[8]  Hossein Abdizadeh,et al.  Development of high-performance A356/nano-Al2O3 composites , 2009 .

[9]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[10]  Liang-Ying Wei,et al.  A GA-weighted ANFIS model based on multiple stock market volatility causality for TAIEX forecasting , 2013, Appl. Soft Comput..

[11]  T. N. Claytor,et al.  Response of aluminium-infiltrated boron carbide cermets to shock wave loading , 1994, Journal of Materials Science.

[12]  Ponnuthurai Nagaratnam Suganthan,et al.  Two-lbests based multi-objective particle swarm optimizer , 2011 .

[13]  Robin A. L. Drew,et al.  Preparation of nickel-coated powders as precursors to reinforce MMCs , 2000 .

[14]  Mohsen Ostad Shabani,et al.  Process conditions optimization in Al–Cu alloy matrix composites , 2012 .

[15]  Ling Zhong,et al.  Deformation Behavior and Microstructure Effect in 2124Al/SiCp Composite , 2000 .

[16]  Carlos Enrique Schvezov,et al.  Numerical Modeling of the Interaction between a Foreign Particle an Solidifying Crystalline Interface , 2012 .

[17]  J. Kennedy,et al.  Population structure and particle swarm performance , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[18]  Mohsen Ostad.  The GA Optimization Performance in the Microstructure and Mechanical Properties of MMNCs , 2012 .

[19]  Jonathan E. Fieldsend,et al.  Optimizing Decision Trees Using Multi-objective Particle Swarm Optimization , 2009 .

[20]  Ali Mazahery,et al.  Investigation on mechanical properties of nano-Al2O3-reinforced aluminum matrix composites , 2011 .

[21]  Mohsen Ostad Shabani,et al.  Modification Mechanism and Microstructural Characteristics of Eutectic Si in Casting Al-Si Alloys: A Review on Experimental and Numerical Studies , 2014 .

[22]  Mohsen Ostad Shabani,et al.  Application of GA to optimize the process conditions of Al Matrix nano-composites , 2013 .

[23]  W. Kool,et al.  Properties of semisolid aluminium matrix composites , 1994 .

[24]  Mohsen Ostad Shabani,et al.  Prediction of Mechanical Properties of Cast A356 Alloy as a Function of Microstructure and Cooling Rate , 2011 .

[25]  Andries Petrus Engelbrecht,et al.  A study of particle swarm optimization particle trajectories , 2006, Inf. Sci..

[26]  Riaan Brits.  Niching strategies for particle swarm optimization , 2005 .

[27]  S. T. Hall.  Processing of Ceramic and Metal Matrix Composites , 1990 .

[28]  D.Y. Ding,et al.  Fabrication of 6061 aluminium alloy/Al18B4O33(w) composite by using sol–gel alumina binder , 2001 .

[29]  Carlos Enrique Schvezov,et al.  Influence of the Shape of the Particles in the Solidification of Composite Materials , 2012 .

[30]  Nidhi Arora,et al.  Reckoner for health risk and insurance premium using adaptive neuro-fuzzy inference system , 2012, Neural Computing and Applications.

[31]  Mohsen Ostad Shabani,et al.  Modelling of mechanical properties of cast A356 alloy , 2011 .

[32]  Fuat Demir,et al.  Stress–strain modeling of high-strength concrete by the adaptive network-based fuzzy inference system (ANFIS) approach , 2013, Neural Computing and Applications.

[33]  Mohsen Ostad Shabani,et al.  The accuracy of various training algorithms in tribological behavior modeling of A356-B4C composites , 2011 .

[34]  Mohsen Ostad Shabani,et al.  Optimization of Al Matrix Reinforced with B4C Particles , 2013 .

[35]  Mohsen Ostad Shabani,et al.  Strategic developments to improve the optimization performance with efficient optimum solution and produce high wear resistance aluminum–copper alloy matrix composites , 2013, Neural Computing and Applications.

[36]  M. Surappa,et al.  Processing and compressive strength of Al–Li–SiCp composites fabricated by a compound billet technique , 2009 .

[37]  S. V. Kamat,et al.  Tensile behaviour of powder metallurgy processed (Al–Cu–Mg)/SiCp composites , 2001 .

[38]  M. O. Shabani,et al.  Application of a linearly decreasing weight particle swarm to optimize the process conditions of al matrix nanocomposites , 2012, Metallurgist.

[39]  Ralph R. Martin,et al.  A Sequential Niche Technique for Multimodal Function Optimization , 1993, Evolutionary Computation.

[40]  Mohsen Ostad Shabani,et al.  Prediction performance of various numerical model training algorithms in solidification process of A356 matrix composites , 2012 .

[41]  Mohsen Ostad Shabani,et al.  The numerical modeling of abrasion resistance in casting aluminum–silicon alloy matrix composites , 2012 .

[42]  B. Kieback,et al.  Friction and wear of copper–graphite composites made with Cu-coated and uncoated graphite powders , 2002 .

[43]  M. O. Shabani,et al.  Erratum to: Application of a linearly decreasing weight particle swarm to optimize the process conditions of al matrix nanocomposites , 2012, Metallurgist.

[44]  T. E. Scott,et al.  Processing and properties of metal matrix composites containing discontinuous reinforcement , 1989 .

[45]  Mohsen Ostad Shabani,et al.  Microstructural prediction of cast A356 alloy as a function of cooling rate , 2011 .

[46]  Carlos Enrique Schvezov,et al.  A numerical model study of the effect of interface shape on particle pushing , 2012 .

[47]  Mohsen Ostad Shabani,et al.  Artificial Intelligence in numerical modeling of nano sized ceramic particulates reinforced metal matrix composites , 2012 .

[48]  Mohsen Ostad Shabani,et al.  A comparative study on abrasive wear behavior of semisolid–liquid processed Al–Si matrix reinforced with coated B4C reinforcement , 2012, Transactions of the Indian Institute of Metals.

[49]  Michael N. Vrahatis,et al.  Detecting resonances in conservative maps using evolutionary algorithms , 2009 .

[50]  Mohsen Ostad Shabani,et al.  A356 Reinforced with Nanoparticles: Numerical Analysis of Mechanical Properties , 2012 .

[51]  Mohsen Ostad Shabani,et al.  Development of the principle of simulated natural evolution in searching for a more superior solution: Proper selection of processing parameters in AMCs , 2013 .

[52]  Mohsen Ostad Shabani,et al.  The enhancement of wear properties of squeeze-cast A356 composites reinforced with B4C particulates , 2012 .

[53]  A. Noorul Haq,et al.  Optimization of friction welding parameters using evolutionary computational techniques , 2009 .

[54]  Jing J. Liang,et al.  Comprehensive learning particle swarm optimizer for global optimization of multimodal functions , 2006, IEEE Transactions on Evolutionary Computation.

[55]  M. Hashmi,et al.  Particle distribution in cast metal matrix composites—Part I , 2002 .

[56]  Mohsen Ostad Shabani,et al.  Concurrent fitness evaluations in searching for the optimal process conditions of Al matrix nanocomposites by linearly decreasing weight , 2013 .

[57]  Carlos Enrique Schvezov,et al.  Columnar-to-Equiaxed Transition in Metal-Matrix Composites Reinforced with Silicon Carbide Particles , 2013 .

[58]  Manoj Gupta,et al.  Effect of limited matrix–reinforcement interfacial reaction on enhancing the mechanical properties of aluminium–silicon carbide composites , 2001 .

[59]  Lifeng Zhang,et al.  CFD Modeling and Simulation in Materials Processing 2016 , 2016 .

[60]  Carlos Enrique Schvezov,et al.  Modeling the interaction of convex solidifying interfaces with spherical particles , 2012 .