Artificial neural network-based multiobjective optimization of mechanical alloying process for synthesizing of metal matrix nanocomposite powder

Materials and Manufacturing Processes

Volumn 27, Issue 1, 2012, Pages 33-42

  Dashtbayazi, M R ^a  

^a UNIVERSITY OF BIRJAND   (Iran)

Author keywords

Aluminum; Crystallite size; Lattice strain; Mechanical alloying; Modeling; Multiobjective; Nanocomposite; Neural network; Optimization

Indexed keywords

ALLOYING PROCESS; ALUMINUM MATRIX; ARTIFICIAL NEURAL NETWORK; CONTOUR PLOT; GRADIENT DESCENT; LATTICE STRAIN; METAL MATRIX NANO COMPOSITES; MILLING TIME; MULTI LAYER PERCEPTRON; MULTI OBJECTIVE; NANOCOMPOSITE POWDER; NETWORK-BASED; OPTIMIZATION PROCEDURES; PATTERN SEARCH; PROCESS VARIABLES; RESPONSE SURFACE; WEIGHT RATIOS;

ALUMINUM; CRYSTALLITE SIZE; MECHANICAL ALLOYING; METALLIC MATRIX COMPOSITES; MILLING (MACHINING); MODELS; NANOCOMPOSITES; NEURAL NETWORKS; OPTIMIZATION; POWDER METALS; SILICON CARBIDE;

MULTIOBJECTIVE OPTIMIZATION;

EID: 84856271779     PISSN: 10426914     EISSN: 15322475     Source Type: Journal    
DOI: 10.1080/10426914.2010.523917     Document Type: Article

References (32)

1. 3 nanocomposite prepared by milling and hot pressing. Materials and Design 2010, 31, 4777-4785.
2. 3 particulates developed by wet attrition milling and hot extrusion. Materials and Design 2010, 31, 3880-3884.
3. 3 dispersed Cu-matrix composite using ball milled precursor. Materials and Manufacturing Processes 2007, 22, 516-524. (Pubitemid 46572979)
4. Nakhjavani, O.B.; Ghoreishi, M. Multi-criteria optimization of laser percussion drilling process using artificial neural network model combined with genetic algorithm. Materials and Manufacturing Processes 2006, 21, 11-18. (Pubitemid 43024397)
5. Chaira, D.; Sangal, S.; Mishra, B.K. Synthesis of aluminium- cementite metal matrix composite by mechanical alloying. Materials and Manufacturing Processes 2007, 22, 492-496. (Pubitemid 46572974)
6. Dashtbayazi, M.R. Characterization of Al=SiC nanocomposite prepared by mechanical alloying process using artificial neural network model. Materials and Manufacturing Processes 2008, 23, 37-45. (Pubitemid 350284470)
7. Hewitt, S.A.; Kibble, K.A. Effects of ball milling time on the synthesis and consolidation of nanostructured WC-Co composites. International Journal of Refractory Metals and Hard Materials 2009, 27, 937-948.
8. Ansary Yar, A.; Montazerian, M.; Abdizadeh, H.; Baharvandi, H.R. Microstructure and mechanical properties of aluminum alloy matrix composite reinforced with nanoparticle MgO. Journal of Alloys and Compounds 2009, 484, 400-404.
9. Maurice, D.; Courtney, T.H. Modeling of the mechanical alloying process. JOM 1992, 10-14. (Pubitemid 23568009)
10. Courtney, T.H.; Maurice, D. Process modeling of the mechanics of mechanical alloying. Scripta Materialia 1996, 34, 5-11. (Pubitemid 126366724)
11. Mehta, R.; Sahay, S.S.; Datta, A.; Chodha, A. Neural network models for industrial batch annealing operation. Materials and Manufacturing Processes 2008, 23, 203-208.
12. Ciurana, J.; Arias, G.; Ozel, T. Neural network modeling and particle swarm optimization (PSO) of process parameters in pulsed laser micromachining of hardened AISI H13 steel. Materials and Manufacturing Processes 2009, 24, 358-368.
13. Vasudevan, M. Soft computing techniques in stainless steel welding. Materials and Manufacturing Processes 2009, 24, 209-218.
14. Kranthi, G.; Satapathy, A. Evaluation and prediction of wear response of pine wood dust filled epoxy composites using neural computation. Computational Materials Science 2010, 49, 609-614.
15. Yassar, R.S.; AbuOmar, O.; Hansen, E.; Horstemeyer, M.F. On dislocation-based artificial neural network modeling of flow stress. Materials and Design 2010, 31, 3683-3689.
16. Mandal, S.; Sivaprasad, P.V.; Barat, P.; Raj, B. An overview of neural network based modeling in alloy design and thermomechanical processing of austenitic stainless steels. Materials and Manufacturing Processes 2009, 24, 219-224.
17. Vanderplaats, G.N. Numerical Optimization Techniques for Engineering Design; McGraw-Hill: New York, 1984.
18. Yi, J.Z.; Lee, P.D.; Lindley, T.C.; Fukui, T. Statistical modeling of microstructure and defect population effects on the fatigue performance of cast A356-T6 automotive components. Materials Science and Engineering A 2006, 432, 59-68. (Pubitemid 44276053)
19. Dashtbayazi, M.R.; Shokuhfar, A.; Simchi, A. Artificial neural network modeling of mechanical alloying process for synthesizing of metal matrix nanocomposite powders. Materials Science and Engineering A 2007, 466, 274-283. (Pubitemid 47046596)
20. White, F.M. Fluid Mechanics; McGraw-Hill: New York, 1979.
21. Rao, S.S. Engineering Optimization Theory and Practice, 4th edition; John Wiley & Sons: New York, 2009.
22. The Math Works, Inc. Simulink® Response Optimization Toolbox (version 2.3), MATLAB® 7.1 (14th Release); 2005.
23. Zuhailawati, H.; Mahani, Y. Effects of milling time on hardness and electrical conductivity of in situ Cu-NbC composite produced by mechanical alloying. Journal of Alloys and Compounds 2009, 476, 142-146.
24. Sasaki, T.T.; Ohkubo, T.; Hono, K. Microstructure and mechanical properties of bulk nanocrystalline Al-Fe alloy processed by mechanical alloying and spark plasma sintering. Acta Materialia 2009, 57, 3529-3538.
25. Dashtbayazi, M.R.; Shokuhfar, A. Statistical modeling of the mechanical alloying process for producing of Al=SiC nanocomposite powders. Computational Materials Science 2007, 40, 466-479. (Pubitemid 47380299)
26. Suryanarayana, C. Mechanical Alloying and Milling; Marcel Dekker: New York, 2004.
27. Suryanarayana, C. Mechanical alloying and milling. Progress in Materials Science 2001, 46, 1-184. (Pubitemid 32071564)
28. Coello, C.A.C.; Becerra, R.L. Evolutionary multiobjective optimization in materials science and engineering. Materials and Manufacturing Processes 2009, 24, 119-129.
29. Pettersson, F.; Biswas, A.; Sen, P.K.; Saxén, H.; Chakraborti, N. Analyzing leaching data for low-grade manganese ore using neural nets and multiobjective genetic algorithms. Materials and Manufacturing Processes 2009, 24, 320-330.
30. Goh, C.K.; Tan, K.C. Evolutionary Multi-objective Optimization in Uncertain Environments; Springer-Verlag: Berlin, 2009.
31. Eiben, A.E.; Smith, J.E. Introduction to Evolutionary Computing; Springer: Berlin, 2003.
32. Coello, C.A.C.; Lamont, G.B.; Van Veldhuizen, D.A. Evolutionary Algorithms for Solving Multi-Objective Problems, 2nd ed.; Springer: New York, 2007.