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Theory and Practice of Material Development Under Imperfect Information

  • M. B. Babanli
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 896)

Abstract

Material development is an important research problem in material science and engineering. Nowadays, computational approaches to these problems are used to alternate natural experiments. These approaches include data mining, machine learning and computational intelligence tools that rely on big data on material characteristics collected over long period experiments. One of the important issues in solving these problems is imperfect nature of information. In the present study we outline fuzzy logic and Z-number concept-based computational methodologies for material synthesis and selection to account for imprecision and partial reliability of relevant information. Several examples are provided to confirm validity of the study.

Keywords

Material synthesis Material selection Big data Decision making Fuzzy logic Z-number 

References

  1. 1.
    Aliev, R.A., Alizadeh, A.V., Huseynov, O.H.: The arithmetic of discrete Z-numbers. Inform. Sci. 290, 134–155 (2015)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Aliev, R.A., Aliev, R.R.: Soft Computing and its Application. World Scientific, New Jersey (2001)CrossRefGoogle Scholar
  3. 3.
    Averill, B.A., Eldredge, P.: Principles of General Chemistry. McGraw-Hill Education, New York City (2012)Google Scholar
  4. 4.
    Babanli, M.B., Huseynov, V.M.: Z-number-based alloy selection problem. Procedia Comput. Sci. 102, 183–189 (2016)CrossRefGoogle Scholar
  5. 5.
    Babanli, M.B.: Synthesis of new materials by using fuzzy and big data concepts. Procedia Comput Sci 120, 104–111 (2017)CrossRefGoogle Scholar
  6. 6.
    Amalgam, D.: A Scientific Review and Recommended Public Health Service Strategy for Research, Education and Regulation Final Report of the Subcommittee on Risk Management of the Committee to Coordinate Environmental Health and Related Programs Public Health Service. Department of Health and Human Services Public Health Service (1993) https://health.gov/environment/amalgam1/selection.htm
  7. 7.
  8. 8.
    Frenzel, J., Wieczorek, A., Opahle, I., Maa, B., Drautz, R., Eggeler, G.: On the effect of alloy composition on martensite start temperatures and latent heats in Ni–Ti-based shape memory alloys. Acta Mater. 90, 213–231 (2015)CrossRefGoogle Scholar
  9. 9.
    Hashimoto, K., Kimura, M., Mizuhara, Y.: Alloy design of gamma titanium aluminides based on phase diagrams. Intermetallics 6(7–8), 667–672 (1998)CrossRefGoogle Scholar
  10. 10.
    Kóczy, L.T.: Approximate reasoning by linear rule interpolation and general approximation. Int. J. Approx. Reason. 9(3), 197–225 (1993)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Kosmač, A.: Factors affecting material selection for high temperature applications – review (2017). https://steelmehdipour.net/wp-content/uploads/2017/02/Factors-affecting-material-selection-for-high-temperature-applications.pdf
  12. 12.
    Laidler, K.J., Meiser, J.H.: Physical Chemistry. Oxford University Press, Oxford (1995)Google Scholar
  13. 13.
    Larson, E.: Thermoplastic Material Selection, a Practical Guide. William Andrew, London (2015)Google Scholar
  14. 14.
    Papon, P., Leblond, J., Meijer, P.H.E.: The Physics of Phase Transition: Concepts and Applications. Springer, Berlin (2002).  https://doi.org/10.1007/3-540-33390-8CrossRefzbMATHGoogle Scholar
  15. 15.
    Petrucci, R.H., Harwood, W.S., Herring, F.G.: General Chemistry. Principles and Modern Applications. Prentice Hall, Upper Saddle River (2001)Google Scholar
  16. 16.
    Predel, B., Hoch, M., Pool, M.: Phase Diagrams and Heterogeneous Equilibria: A Practical Introduction. Springer, Berlin (2004).  https://doi.org/10.1007/978-3-662-09276-7CrossRefGoogle Scholar
  17. 17.
    Preuss, M., Wessing, S., Rudolph, G., Sadowski, G.: Solving phase equilibrium problems by means of avoidance-based multiobjectivization. In: Kacprzyk, J., Pedrycz, W. (eds.) Springer Handbook of Computational Intelligence. Springer, Heidelberg (2015).  https://doi.org/10.1007/978-3-662-43505-2_58CrossRefGoogle Scholar
  18. 18.
    Stan, M., Reardon, B.J.: A Bayesian approach to evaluating the uncertainty of thermodynamic data and phase diagrams. Comput. Coupling Phase Diagr. Thermochem. 27(3), 319–323 (2003)CrossRefGoogle Scholar
  19. 19.
    Welling, D.A.: A fuzzy logic material selection methodology for renewable ocean energy applications. Proquest, Umi Dissertation Publishing, 154 p. (2011)Google Scholar
  20. 20.
    Yazdani, M., Graeml, F.R.: VIKOR and its applications: a state-of-the-art survey. Int. J. Strat. Decis. Sci. 5(2), 56–83 (2014)CrossRefGoogle Scholar
  21. 21.
    Zadeh, L.A.: A note on Z-numbers. Inform. Sci. 181, 2923–2932 (2011)MathSciNetCrossRefGoogle Scholar
  22. 22.
    Zadeh, L.A.: Fuzzy Sets. Inform. Control 8, 338–353 (1965)CrossRefGoogle Scholar
  23. 23.
    Zadeh, L.A.: Interpolative reasoning in fuzzy logic and neural network theory. In: Proceedings of the First IEEE International Conference Fuzzy, San-Diego, CA, March 1992Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Azerbaijan State University of Oil and IndustryBakuAzerbaijan

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