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Neural Computing and Applications

, Volume 31, Supplement 2, pp 723–732 | Cite as

Prediction of the mean grain size of MA-synthesized nanopowders by artificial neural networks

  • Mohammad Zakeri
  • Ali NazariEmail author
Original Article
  • 216 Downloads

Abstract

In this work, mean grain size of synthesized nanomaterials (such as MoSi2–TiC and MoSi2–SiC), which were produced by mechanical alloying, has been modeled by artificial neural networks. A total number of 103 data were gathered from the previous works, trained, validated and tested by the built networks. The used data as inputs were the method of calculation of the mean grain size, milling time, annealing temperature, produced phases after mechanical alloying, vial speed and ball-to-powder ratio. The value of the output layer was the mean grain size. The obtained results from the testing phase of the trained networks showed that the models are capable of predicting the mean grain size of the mechanical-alloyed synthesized materials in the considered range.

Keywords

Mechanical alloying Synthesized materials Nanopowders Artificial neural networks 

References

  1. 1.
    Wang H, Ouyang LZ, Zeng MQ, Zhu M (2004) Direct synthesis of MgCNi3 by mechanical alloying. Scr Mater 50:1471–1474CrossRefGoogle Scholar
  2. 2.
    Zakeri M, Ramezani M (2012) Synthesis of MoSi2–TiC nanocomposite powder via mechanical alloying. Ceram Int 38:1353–1357CrossRefGoogle Scholar
  3. 3.
    Zakeri M, Ahmadi M (2012) Mechanochemical synthesis of MoSi2–SiC nanocomposite powder. Ceram Int 38:2977–2982CrossRefGoogle Scholar
  4. 4.
    Zakeri M, Rahimipour MR, Sadrnezhad SKh (2010) In situ synthesis of FeSi–Al2O3 nanocomposite powder by mechanical alloying. J Alloy Compd 492:226–230CrossRefGoogle Scholar
  5. 5.
    Yazdani-rad R, Mirvakili SA, Zakeri M (2010) Synthesis of (Mo1−x–Crx)Si2 nanostructured powders via mechanical alloying and following heat treatment. J Alloy Compd 489:379–383CrossRefGoogle Scholar
  6. 6.
    Zakeri M, Rahimipour MR, Khanmohammadian A (2008) Mechanically activated synthesis of nanocrystalline ternary carbide Fe3Mo3C. Mater Sci Eng A 492:311–316CrossRefGoogle Scholar
  7. 7.
    Zakeri M, Allahkaramia M, Kavei Gh, Khanmohammadian A, Rahimipour MR (2009) Synthesis of nanocrystalline Bi2Te3 via mechanical alloying. J Mater Process Tech 209:96–101CrossRefGoogle Scholar
  8. 8.
    Zakeri M, Vakili-Ahrarirudi A (2012) Effect of milling speed and shaping method on mechanical properties of nanostructure bulked aluminum. Mater Des 37:487–490CrossRefGoogle Scholar
  9. 9.
    Zakeri M, Yazdani-Rad R, Enayati MH, Rahimipour MR, Mobasherpour I (2007) Mechanochemical reduction of MoO3/SiO2 powder mixtures by Al and carbon for the synthesis of nanocrystalline MoSi2. J Alloy Compd 430:170–174CrossRefGoogle Scholar
  10. 10.
    Zakeri M, Yazdani-Rad R, Enayati MH, Rahimipour MR (2005) Synthesis of nanocrystalline MoSi2 by mechanical alloying. J Alloy Compd 403:258–261CrossRefGoogle Scholar
  11. 11.
    Zakeri M, Rahimipour MR, Sadrnezhad SKh, Yazdanni-rad R (2010) Preparation of alumina–tungsten carbide nanocomposite by mechano-chemical, reduction of WO3 with aluminum and graphite. J Alloy Compd 491:203–208CrossRefGoogle Scholar
  12. 12.
    Zakeri M, Rahimipour MR, Sadrnezhad SK, Yazdani-rad R (2009) Preparation of Al2O3–TiC nanocomposite by mechano-chemical reduction of TiO2 with aluminum and graphite. J Alloy Compd 481:320–325CrossRefGoogle Scholar
  13. 13.
    Zakeri M, Rahimipour MR, Khanmohammadian A (2008) Preparation of NiAl–TiC nanocomposite by mechanical alloying. J Mater Sci 43:6912–6919CrossRefGoogle Scholar
  14. 14.
    Zakeri M, Allahkarami M, Kavei Gh, Khanmohammadian A, Rahimipour MR (2008) Low temperature synthesis of nanocrystalline Sb2Te3 by mechanical alloying. J Mater Sci 43:1638–1643CrossRefGoogle Scholar
  15. 15.
    Rietveld HM (1969) A profile refinement method for nuclear and magnetic structures. J Appl Crystallogr 2:65–71CrossRefGoogle Scholar
  16. 16.
    Williamson GK, Hall WH (1953) X-ray line broadening from filed aluminum and wolfram. Acta Metal 1:22–31CrossRefGoogle Scholar
  17. 17.
    Cullity BD (1977) Elements of X-ray diffraction, 2nd edn. Addison–Wesley, ReadingGoogle Scholar
  18. 18.
    Sarıdemir M, Topcu IB, Ozcan F, Severcan MH (2009) Prediction of long-term effects of GGBFS on compressive strength of concrete by artificial neural networks and fuzzy logic. Constr Build Mater 23:1279–1286CrossRefGoogle Scholar
  19. 19.
    Sarıdemir M (2009) Prediction of compressive strength of concretes containing metakaolin and silica fume by artificial neural networks. Adv Eng Softw 40:350–355CrossRefzbMATHGoogle Scholar
  20. 20.
    Sarıdemir M (2009) Predicting the compressive strength of mortars containing metakaolin by artificial neural networks and fuzzy logic. Adv Eng Softw 40:920–927CrossRefzbMATHGoogle Scholar
  21. 21.
    Dashtbayazi MR, Shokuhfar A, Simchi A (2007) Artificial neural network modeling of mechanical alloying process for synthesizing of metal matrix nanocomposite powders. Mater Sci Eng A 466:274–283CrossRefGoogle Scholar
  22. 22.
    Ghaisari J, Jannesari H, Vatani M (2012) Artificial neural network predictors for mechanical properties of cold rolling products. Adv Eng Softw 45(1):91–99CrossRefGoogle Scholar
  23. 23.
    Nazari A, Riahi S (2010) Computer-aided prediction of physical and mechanical properties of high strength cementitious composite containing Cr2O3 nanoparticles. NANO 5(5):301–318CrossRefGoogle Scholar
  24. 24.
    Nazari A, Riahi S (2011) Artificial neural networks to prediction total specific pore volume of geopolymers produced from waste ashes. Neural Comput Appl. doi: 10.1007/s00521-011-0760-x
  25. 25.
    Nazari A, Sedghi A, Didehvar N (2012) Modeling impact resistance of aluminum–epoxy-laminated composites by artificial neural networks. J Compos Mater 46(13)1593–1605CrossRefGoogle Scholar
  26. 26.
    Nazari A, Milani AA, Zakeri M (2011) Modeling ductile to brittle transition temperature of functionally graded steels by artificial neural networks. Comput Mater Sci 50:2028–2037CrossRefGoogle Scholar
  27. 27.
    Bilim C, Atis CD, Tanyildizi H, Karahan O (2009) Predicting the compressive strength of ground granulated blast furnace slag concrete using artificial neural network. Adv Eng Softw 40:334–340CrossRefzbMATHGoogle Scholar
  28. 28.
    Suratgar AA, Tavakoli MB, Hoseinabadi A (2005) Modified Levenberg–Marquardt method for neural networks training. World Acad Sci Eng Technol 6:46–48Google Scholar
  29. 29.
    Guzelbey IH, Cevik A, Erklig A (2006) Prediction of web crippling strength of cold-formed steel sheetings using neural networks. J Constr Steel Res 62:962–973CrossRefGoogle Scholar
  30. 30.
    Topcu IB, Sarıdemir M (2008) Prediction of compressive strength of concrete containing fly ash using artificial neural network and fuzzy logic. Comp Mater Sci 41(3):305–311CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Limited 2012

Authors and Affiliations

  1. 1.Ceramic DepartmentMaterials and Energy Research CenterKarajIran
  2. 2.Department of Materials Science, Saveh BranchIslamic Azad UniversitySavehIran

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