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Review on the New Materials Design Methods

  • M. B. Babanli
  • F. Prima
  • P. Vermaut
  • L. D. Demchenko
  • A. N. Titenko
  • S. S. Huseynov
  • R. J. Hajiyev
  • V. M. Huseynov
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 896)

Abstract

For a long time experimental approach was main method for material design. However, experimental approach has many drawbacks. With the development of the computing sciences, a new era of synthesis of alloys or different materials began. Scientists proposed and developed various approaches for the synthesis of new alloys which relies on phase diagrams, Thermo-Calc, machine learning, neural network and fuzzy concepts.

Keywords

Materials design Alloys Neural network Fuzzy logic Z-number theory 

References

  1. 1.
    Hashimoto, K., Kimura, M., Mizuhara, Y.: Alloy design of gammatitanium aluminides based on phase diagrams. Intermetallics 6, 667–672 (1998)CrossRefGoogle Scholar
  2. 2.
    Andersson, J.O., Helander, T., Höglund, L., Shi, P., Sundman, B.: Thermo-Calc & DICTRA, computational tools for materials science. Calphad 26, 273–312 (2002)CrossRefGoogle Scholar
  3. 3.
    Weinert, M., Schneider, G., Podloucky, R., Redinger, J.: FLAPW: applications and implementations. J. Phys.: Condens. Matter 21(8), 084201 (2009)Google Scholar
  4. 4.
    Abreu, M.P.: On the development of computational tools for the design of beam assemblies for Boron neutron capture therapy. J. Comput. Aided Mater. Des. 14, 235–251 (2007)CrossRefGoogle Scholar
  5. 5.
    Takahashi, K., Tanaka, Y.: Material synthesis and design from first principle calculations and machine learning. Comput. Mater. Sci. 112, 364–367 (2016)CrossRefGoogle Scholar
  6. 6.
    Elton, D.C., Boukouvalas, Z., Butrico, M.S., Fuge, M.D., Chung, P.W.: Applying machine learning techniques to predict the properties of energetic materials (2018)Google Scholar
  7. 7.
    Dehghannasiri, R., Xue, D., Balachandran, P.V., Yousefi, M.R., Dalton, L.A., Lookman, T., Dougherty, E.R.: Optimal experimental design for materials discovery. Comput. Mater. Sci. 129, 311–322 (2017)CrossRefGoogle Scholar
  8. 8.
    Hey, T., Tansley, S., Tolle, K. (eds.): The Fourth Paradigm: Data-Intensive Scientific Discovery. Microsoft Corporation, p. 287 (2009)Google Scholar
  9. 9.
    White, A.A.: Big data are shaping the future of materials science. MRS Bull. 38, 594–595 (2013)CrossRefGoogle Scholar
  10. 10.
    Fellet, M.: Big Data Analytics Deliver Materials Science Insights (2017). http://www.lindau-nobel.org/blog-big-data-analytics-deliver-materials-science-insights/
  11. 11.
    Hill, J., Mulholland, G., Persson, K., Seshadri, R., Wolverton, C., Meredig, B.: Materials science with large-scale data and informatics: unlocking new opportunities. MRS Bull. 41, 399–409 (2016)CrossRefGoogle Scholar
  12. 12.
  13. 13.
  14. 14.
  15. 15.
  16. 16.
    Seshadri, R., Sparks, T.D.: Perspective: interactive material property databases through aggregation of literature data. APL Mater. 4(5), 053206 (2016)CrossRefGoogle Scholar
  17. 17.
    Belsky, A., Hellenbrandt, M., Karen, V.L., Luksch, P.: Acta Crystallogr. Sect. B 58, 364 (2002)CrossRefGoogle Scholar
  18. 18.
    Allen, F.H.: Acta Crystallogr. Sect. B 58, 380 (2002)CrossRefGoogle Scholar
  19. 19.
    Downs, R.T., Hall-Wallace, M.: Am. Miner. 88, 247 (2003)CrossRefGoogle Scholar
  20. 20.
    Gražulis, S., Chateigner, D., Downs, R.T., Yokochi, A.F.T., Quirós, M., Lutterotti, L., Manakova, E., Butkus, J., Moeck, P., Le Bail, A.: J. Appl. Crystallogr. 42, 726 (2009)CrossRefGoogle Scholar
  21. 21.
    Villars, P.: Pearson’s Crystal Data: Crystal Structure Database for Inorganic Compounds (2007)Google Scholar
  22. 22.
    Berman, H.M., Westbrook, J., Feng, Z., Gilliland, G., Bhat, T., Weissig, H., Shindyalov, I.N., Bourne, P.E.: Nucleic Acid Res. 28, 235 (2000)CrossRefGoogle Scholar
  23. 23.
  24. 24.
    Jain, A., Persson, K.A., Ceder, G.: The materials genome initiative: data sharing and the impact of collaborative ab initio databases. J. APL Mater. 4(5), 1–14 (2016)Google Scholar
  25. 25.
    Sumpter, B.G., Vasudevan, R.K., Potok, T., Kalinin, S.V.: A bridge for accelerating materials by design. NPJ Comput. Mater. 1, 15008 (2015)CrossRefGoogle Scholar
  26. 26.
    Christodoulou, J.A.: Integrated computational materials engineering and materials genome initiative: accelerating materials innovation. Adv. Mater. Process. 171(3), 28–31 (2013)Google Scholar
  27. 27.
    White, A.A.: Universities prepare next-generation workforce to benefit from the materials genome initiative. MRS Bull. 38, 673–674 (2013)CrossRefGoogle Scholar
  28. 28.
    Olson, G.B., Kuehmann, C.J.: Materials genomics: from CALPHAD to flight. Scr. Mater. 70, 25–30 (2014)CrossRefGoogle Scholar
  29. 29.
    White, A.A.: Interdisciplinary collaboration, robust funding cited as key to success of materials genome initiative program. MRS Bull. 38, 894–896 (2013)CrossRefGoogle Scholar
  30. 30.
    White, A.: Workshop makes recommendations to increase diversity in materials science and engineering. MRS Bull. 38, 120–122 (2013)Google Scholar
  31. 31.
    Ceder, G., Hautier, G., Jain, A., Ong, S.P.: Recharging lithium battery research with first-principles methods. MRS Bull. 36, 185–191 (2011)Google Scholar
  32. 32.
    Zadeh, L.A.: Fuzzy logic = computing with words. IEEE Trans. Fuzzy Syst. 4(2), 103–111 (1996)CrossRefGoogle Scholar
  33. 33.
    Aliev, R.A., Aliev, R.R.: Soft Computing and Its Application. World Scientific, New Jersey (2001)CrossRefGoogle Scholar
  34. 34.
    Pedrycz, W., Peters, J.F.: Computational Intelligence in Software Engineering. Advances in Fuzzy Systems, Applications and Theory, vol. 16. World Scientific, Singapoure (1998)Google Scholar
  35. 35.
    Babanli, M.B., Huseynov, V.M.: Z-number-based alloy selection problem. In: 12th International Conference on Application of Fuzzy Systems and Soft Computing, ICAFS 2016, Vienna, Austria. Procedia Comput. Sci. 102, 183–189 (2016)CrossRefGoogle Scholar
  36. 36.
    Chen, S.-M.: A new method for tool steel materials selection under fuzzy environment. Fuzzy Sets Syst. 92, 265–274 (1997)CrossRefGoogle Scholar
  37. 37.
    Cheng, J., Feng, Y., Tan, J., Wei, W.: Optimization of injection mold based on fuzzy moldability evaluation. J. Mater. Process. Technol. 21, 222–228 (2008)CrossRefGoogle Scholar
  38. 38.
    Lee, Y.-H., Kopp, R.: Application of fuzzy control for a hydraulicforging machine. Fuzzy Sets Syst. 99, 99–108 (2001)CrossRefGoogle Scholar
  39. 39.
    Elishakoff, I., Ferracuti, B.: Fuzzy sets based interpretation of the safety factor. Fuzzy Sets Syst. 157, 2495–2512 (2006)MathSciNetCrossRefGoogle Scholar
  40. 40.
    Rao, H.S., Mukherjee, A.: Artificial neural networks for predicting the macromechanical behaviour of ceramic-matrix composites. Comput. Mater. Sci. 5, 307–322 (1996)CrossRefGoogle Scholar
  41. 41.
    Hancheng, Q., Bocai, X., Shangzheng, L., Fagen, W.: Fuzzy neural network modeling of material properties. J. Mater. Process. Technol. 122, 196–200 (2002)CrossRefGoogle Scholar
  42. 42.
    Chen, D., Li, M., Wu, S.: Modeling of microstructure and constitutive relation during super plastic deformation by fuzzy-neural network. J. Mater. Process. Technol. 142, 197–202 (2003)CrossRefGoogle Scholar
  43. 43.
    Odejobi, O.A., Umoru, L.E.: Applications of soft computing techniques in materials engineering: a review. Afr. J. Math. Comput. Sci. Res. 2(7), 104–131 (2009)Google Scholar
  44. 44.
    Datta, S., Chattopadhyay, P.P.: Soft computing techniques in advancement of structural metals. Int. Mater. Rev. 58, 475–504 (2013)CrossRefGoogle Scholar
  45. 45.
    Tajdari, M., Mehraban, A.G., Khoogar, A.R.: Shear strength prediction of Ni–Ti alloys manufactured by powder metallurgy using fuzzy rule-based model. Mater. Des. 31, 1180–1185 (2010)CrossRefGoogle Scholar
  46. 46.
    Babanli, M.B.: Synthesis of new materials by using fuzzy and big data concepts. Procedia Comput. Sci. 120, 104–111 (2017)CrossRefGoogle Scholar
  47. 47.
    Nandi, A.K., Pratihar, D.K.: Automatic design of fuzzy logic controller using a genetic algorithm-to predict power requirement and surface finish in grinding. J. Mater. Process. Technol. 148, 288–300 (2004)CrossRefGoogle Scholar
  48. 48.
    Sakundarini, N., Taha, Z., Abdul-Rashid, S.H., Ghazilla, R.A.R.: Incorporation of high recyclability material selection in computer aided design. Mater. Des. 56, 740–749 (2014)CrossRefGoogle Scholar
  49. 49.
    Morinaga, M., Kato, M., Kamimura, T., Fukumoto, M., Harada, I., Kubo, K.: Theoretical design of b-type titanium alloys. In: Titanium 1992, Science and Technology, Proceedings of 7th International Conference on Titanium, San Diego, CA, USA, pp. 276–283 (1992)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • M. B. Babanli
    • 1
  • F. Prima
    • 2
  • P. Vermaut
    • 2
  • L. D. Demchenko
    • 3
  • A. N. Titenko
    • 4
  • S. S. Huseynov
    • 1
  • R. J. Hajiyev
    • 1
  • V. M. Huseynov
    • 1
  1. 1.Azerbaijan State Oil and Industry UniversityBakuAzerbaijan
  2. 2.Chimie ParisTech, UMR CNRS 7045ParisFrance
  3. 3.National Technical University of Ukraine “KPI”KievUkraine
  4. 4.Institute of Magnetism Under NAS and MES of UkraineKievUkraine

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