Hostname: page-component-cd9895bd7-dk4vv Total loading time: 0 Render date: 2024-12-26T22:17:49.578Z Has data issue: false hasContentIssue false
  • English
  • Français

Application of computational tools in alloy design

Published online by Cambridge University Press:  09 April 2019

Akane Suzuki ,
Chen Shen  and
Natarajan Chennimalai Kumar
Akane Suzuki
Affiliation:
GE Research, USA; suzukia@ge.com
Chen Shen
Affiliation:
GE Research, USA; chens@ge.com
Natarajan Chennimalai Kumar
Affiliation:
GE Research, USA; kumarn@ge.com
Get access

Abstract

Alloy design is critical to achieving the target performance of industrial components and products. In designing new alloys, there are multiple property requirements, including mechanical, environmental, and physical properties, as well as manufacturability and processability. Computational models and tools to predict properties from alloy compositions and to optimize compositions for multiple objectives are essential in enabling efficient, robust alloy design. Data-driven property models by machine learning (ML) are particularly useful in predicting physical properties with relatively simple dependence on composition, and in predicting complex properties that are too difficult for a physics-based model to achieve with desirable accuracy. In this article, we describe examples of ML applications to model coefficient of thermal expansion, creep and fatigue resistance in designing Ni-based superalloys, and optimization methodologies. We also discuss physics-based microstructure models that have been developed for optimizing heat-treatment conditions to achieve desired microstructures.

Keywords

alloychemical compositionmicrostructuresimulationstrength
Type
Computational Design And Development Of Alloys
Information
MRS Bulletin , Volume 44 , Issue 4: Computational design and development of alloys , April 2019 , pp. 247 - 251
Copyright
Copyright © Materials Research Society 2019 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Pollock, T.M., Allison, J.E., Backman, D.G., Boyce, M.C., Gersh, M., Holm, E.A., LeSar, R., Long, M., Powell IV, A.C., Schirra, J.J., Whitis, D.D., Woodward, C., Integrated Computational Materials Engineering: A Transformational Discipline for Improved Competitiveness and National Security (National Research Council, National Academies Press, Washington, DC, 2008).Google Scholar
https://www.mgi.gov.Google Scholar
Pollock, T.M., Tin, S., J. Propul. Power 22, 361 (2006).10.2514/1.18239CrossRefGoogle Scholar
Reed, R.C., The Superalloys: Fundamentals and Applications (Cambridge University Press, Cambridge, UK, 2006).10.1017/CBO9780511541285CrossRefGoogle Scholar
Darolia, R., Int. Mater. Rev. (2018), doi.org/10.1080/09506608.2018.1516713.Google Scholar
Suzuki, A., Gigliotti, M.F.X., Hazel, B.T., Konitzer, D.G., Pollock, T.M., Metall. Mater. Trans. A 41, 947 (2010).10.1007/s11661-009-0169-7CrossRefGoogle Scholar
Patil, S., Huang, S., Karadge, M., Konitzer, D., Suzuki, A., in Superalloys 2016 , Hardy, M., Ed. (Wiley, Hoboken, NJ, 2016), p. 959.10.1002/9781119075646.ch102CrossRefGoogle Scholar
Reed, R.C., Tao, T., Warnken, N., Acta Mater . 57, 5898 (2009).10.1016/j.actamat.2009.08.018CrossRefGoogle Scholar
Schafrik, R.E., Metall. Mater. Trans. B 47, 1505 (2016).10.1007/s11663-016-0655-4CrossRefGoogle Scholar
Guillaume, C.E., Nature 71, 134 (1904).10.1038/071134a0CrossRefGoogle Scholar
Bozzolo, G., del Grosso, M.F., Mosca, H.O., Mater. Lett. 62, 3975 (2008).10.1016/j.matlet.2008.05.026CrossRefGoogle Scholar
Kim, D., Shang, S.-L., Liu, Z.-K., Acta Mater . 60, 1846 (2012).10.1016/j.actamat.2011.12.005CrossRefGoogle Scholar
van Schilfgaarde, M., Abrikosov, I.A., Johansson, B., Nature 400, 46 (1999).10.1038/21848CrossRefGoogle Scholar
Karunaratne, M.S.A., Kyaw, S., Jones, A., Morrell, R., Thomson, R.C., J. Mater Sci. 51, 4213 (2016).10.1007/s10853-015-9554-3CrossRefGoogle Scholar
Sung, P.K., Poirier, D.R., Mater Sci. Eng. A 245, 135 (1998).10.1016/S0921-5093(97)00699-0CrossRefGoogle Scholar
Bano, N., Nganbe, M., J. Mater. Eng. Perform. 22, 952 (2013).10.1007/s11665-012-0398-6CrossRefGoogle Scholar
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, É., J. Mach. Learn. Res. 12, 2825 (2011).Google Scholar
Raschka, S., J. Open Source Softw. 3, 638 (2018).10.21105/joss.00638CrossRefGoogle Scholar
Srivastava, A., Subramaniyan, A.K., Wang, L., ASME Turbo Expo 2015 (ASME, New York, 2015) p. GT2015–43693.Google Scholar
Kumar, N.C., Subramaniyan, A.K., Wang, L., ASME Turbo Expo 2012 (ASME, New York, 2012) p. GT2012–69058.Google Scholar
Shen, C., Suzuki, A., Konitzer, D.G., in Superalloys 2016, Hardy, M., Ed. (Wiley, Hoboken, NJ, 2016) p. 259.Google Scholar
Shen, C., in Modeling Long-Term Creep Performance for Welded Nickel-Base Superalloy Structures for Power Generation Systems (2017), doi:10.2172/1345084.CrossRefGoogle Scholar
Shen, C., in Modeling Creep-Fatigue-Environment Interactions in Steam Turbine Rotor Materials for Advanced Ultra-Supercritical Coal Power Plants (2014), doi:10.2172/1134364.CrossRefGoogle Scholar
Zhao, J.-C., Prog. Mater. Sci. 51, 557 (2006).10.1016/j.pmatsci.2005.10.001CrossRefGoogle Scholar