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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Hashimoto, K., Kimura, M., Mizuhara, Y.: Alloy design of gammatitanium aluminides based on phase diagrams. Intermetallics 6, 667–672 (1998)
Andersson, J.O., Helander, T., Höglund, L., Shi, P., Sundman, B.: Thermo-Calc & DICTRA, computational tools for materials science. Calphad 26, 273–312 (2002)
Weinert, M., Schneider, G., Podloucky, R., Redinger, J.: FLAPW: applications and implementations. J. Phys.: Condens. Matter 21(8), 084201 (2009)
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)
Takahashi, K., Tanaka, Y.: Material synthesis and design from first principle calculations and machine learning. Comput. Mater. Sci. 112, 364–367 (2016)
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)
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)
Hey, T., Tansley, S., Tolle, K. (eds.): The Fourth Paradigm: Data-Intensive Scientific Discovery. Microsoft Corporation, p. 287 (2009)
White, A.A.: Big data are shaping the future of materials science. MRS Bull. 38, 594–595 (2013)
Fellet, M.: Big Data Analytics Deliver Materials Science Insights (2017). http://www.lindau-nobel.org/blog-big-data-analytics-deliver-materials-science-insights/
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)
Seshadri, R., Sparks, T.D.: Perspective: interactive material property databases through aggregation of literature data. APL Mater. 4(5), 053206 (2016)
Belsky, A., Hellenbrandt, M., Karen, V.L., Luksch, P.: Acta Crystallogr. Sect. B 58, 364 (2002)
Allen, F.H.: Acta Crystallogr. Sect. B 58, 380 (2002)
Downs, R.T., Hall-Wallace, M.: Am. Miner. 88, 247 (2003)
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)
Villars, P.: Pearson’s Crystal Data: Crystal Structure Database for Inorganic Compounds (2007)
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)
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)
Sumpter, B.G., Vasudevan, R.K., Potok, T., Kalinin, S.V.: A bridge for accelerating materials by design. NPJ Comput. Mater. 1, 15008 (2015)
Christodoulou, J.A.: Integrated computational materials engineering and materials genome initiative: accelerating materials innovation. Adv. Mater. Process. 171(3), 28–31 (2013)
White, A.A.: Universities prepare next-generation workforce to benefit from the materials genome initiative. MRS Bull. 38, 673–674 (2013)
Olson, G.B., Kuehmann, C.J.: Materials genomics: from CALPHAD to flight. Scr. Mater. 70, 25–30 (2014)
White, A.A.: Interdisciplinary collaboration, robust funding cited as key to success of materials genome initiative program. MRS Bull. 38, 894–896 (2013)
White, A.: Workshop makes recommendations to increase diversity in materials science and engineering. MRS Bull. 38, 120–122 (2013)
Ceder, G., Hautier, G., Jain, A., Ong, S.P.: Recharging lithium battery research with first-principles methods. MRS Bull. 36, 185–191 (2011)
Zadeh, L.A.: Fuzzy logic = computing with words. IEEE Trans. Fuzzy Syst. 4(2), 103–111 (1996)
Aliev, R.A., Aliev, R.R.: Soft Computing and Its Application. World Scientific, New Jersey (2001)
Pedrycz, W., Peters, J.F.: Computational Intelligence in Software Engineering. Advances in Fuzzy Systems, Applications and Theory, vol. 16. World Scientific, Singapoure (1998)
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)
Chen, S.-M.: A new method for tool steel materials selection under fuzzy environment. Fuzzy Sets Syst. 92, 265–274 (1997)
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)
Lee, Y.-H., Kopp, R.: Application of fuzzy control for a hydraulicforging machine. Fuzzy Sets Syst. 99, 99–108 (2001)
Elishakoff, I., Ferracuti, B.: Fuzzy sets based interpretation of the safety factor. Fuzzy Sets Syst. 157, 2495–2512 (2006)
Rao, H.S., Mukherjee, A.: Artificial neural networks for predicting the macromechanical behaviour of ceramic-matrix composites. Comput. Mater. Sci. 5, 307–322 (1996)
Hancheng, Q., Bocai, X., Shangzheng, L., Fagen, W.: Fuzzy neural network modeling of material properties. J. Mater. Process. Technol. 122, 196–200 (2002)
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)
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)
Datta, S., Chattopadhyay, P.P.: Soft computing techniques in advancement of structural metals. Int. Mater. Rev. 58, 475–504 (2013)
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)
Babanli, M.B.: Synthesis of new materials by using fuzzy and big data concepts. Procedia Comput. Sci. 120, 104–111 (2017)
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)
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)
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)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Babanli, M.B. et al. (2019). Review on the New Materials Design Methods. In: Aliev, R., Kacprzyk, J., Pedrycz, W., Jamshidi, M., Sadikoglu, F. (eds) 13th International Conference on Theory and Application of Fuzzy Systems and Soft Computing — ICAFS-2018. ICAFS 2018. Advances in Intelligent Systems and Computing, vol 896. Springer, Cham. https://doi.org/10.1007/978-3-030-04164-9_124
Download citation
DOI: https://doi.org/10.1007/978-3-030-04164-9_124
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-04163-2
Online ISBN: 978-3-030-04164-9
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)