Skip to main content
SpringerLink
Log in

Microstructure-Sensitive Computational Structure-Property Relations in Materials Design

  • Chapter
  • First Online:
Computational Materials System Design

Abstract

We consider the role of computational materials science and mechanics in establishing improved understanding and quantification of structure-property relations for engineered materials. This chapter first addresses key aspects and implications of structure hierarchy in practical systems of interest. We then proceed to discuss aspirations and challenges for designing materials via tailoring of hierarchical structure. It is emphasized that materials design is not equivalent to modeling across scales of material structure hierarchy; the latter provides support for decisions made in design and development of materials in the presence of uncertainty. We next provide compelling reasons to develop microstructure-sensitive multiscale models to facilitate simulation-assisted alloy design. Hierarchical and concurrent multiscale models are defined and contrasted in terms of utility in supporting materials design. Inherent difficulties of inverting complex structure-property relations are discussed, along with some recently developed strategies for relating desired properties to feasible structures, as well as top-down, inductive design exploration.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Institutional subscriptions

References

  1. Adams, B.L., Lyon, M., Henrie, B.: Microstructures by design: linear problems in elastic-plastic design. Int. J. Plast. 20(8–9), 1577–1602 (2004)

    Article  Google Scholar 

  2. Ashby, M.F.: Materials Selection in Mechanical Design, 2nd edn. Butterworth-Heinemann, Oxford (1999)

    Google Scholar 

  3. Billinge, S.J.E., Rajan, K., Sinnott, S.B.: From cyberinfrastructure to cyberdiscovery in materials science: enhancing outcomes in materials research, education and outreach. Report from NSF-sponsored workshop held in Arlington, Virginia, 3–5 Aug (2006)

    Google Scholar 

  4. Butler, G.C., McDowell, D.L.: Polycrystal constraint and grain subdivision. Int. J. Plast. 14(8), 703–717 (1998)

    Article  Google Scholar 

  5. Chen, L., Chen, J., Lebensohn, R., Chen, L.-Q.: An integrated fast Fourier transform-based phase-field and crystal plasticity approach to model recrystallization of three dimensional polycrystals. Comp. Meth. Appl. Mech. Eng. 285, 829–848 (2014)

    Article  Google Scholar 

  6. Chen, P., Zabaras, N.: Uncertainty quantification for multiscale disk forging of polycrystal materials using probabilistic graphical model techniques. Comput. Mater. Sci. 84, 278–292 (2014)

    Article  Google Scholar 

  7. Choi, H.-J.: A robust design method for model and propagated uncertainty. PhD Dissertation, G.W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta (2005)

    Google Scholar 

  8. Choi, H.-J., McDowell, D.L., Allen, J.K., Rosen, D., Mistree, F.: An inductive design exploration method for the integrated design of multi-scale materials and products. J. Mech. Des. 130(3), 031402 (2008)

    Article  Google Scholar 

  9. Ellis, B.D.: Multiscale modeling and design of ultra-high-performance concrete. PhD Dissertation, Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta (2013)

    Google Scholar 

  10. Fast, T., Kalidindi, S.R.: Formulation and calibration of higher-order elastic localization relationships using the MKS approach. Acta Mater. 59, 4595–4605 (2011)

    Article  Google Scholar 

  11. Fast, T., Niezgoda, S.R., Kalidindi, S.R.: A new framework for computationally efficient structure-structure evolution linkages to facilitate high-fidelity scale bridging in multi-scale materials models. Acta Mater. 59(2), 699–707 (2011)

    Article  Google Scholar 

  12. Featherston, C., O’Sullivan, E.: A review of international public sector strategies and roadmaps: a case study in advanced materials. Centre for Science Technology and Innovation, Institute for Manufacturing. University of Cambridge, UK. http://www.ifm.eng.cam.ac.uk/uploads/Resources/Featherston__OSullivan_2014_-_A_review_of_international_public_sector_roadmaps-_advanced_materials_full_report.pdf. (2014). Accessed 24 Sept 2017

  13. Fish, J.: Multiscale Methods: Bridging the Scales in Science and Engineering. Oxford University Press, 1st edn. ISBN 978–0–19-923385-4 (2009)

    Google Scholar 

  14. Fullwood, D.T., Niezgoda, S.R., Adams, B.L., Kalidindi, S.R.: Microstructure sensitive design for performance optimization. Prog. Mater. Sci. 55(6), 477–562 (2010)

    Article  Google Scholar 

  15. Ganapathysubramanian, S., Zabaras, N.: Design across length scales: a reduced-order model of polycrystal plasticity for the control of microstructure-sensitive material properties. Comput. Methods Appl. Mech. Eng. 193(45–47), 5017–5034 (2004)

    Article  Google Scholar 

  16. Geers, M.G.D., Kouznetsova, V.G., Brekelmans, W.A.M.: Multi-scale computational homogenization: trends and challenges. J. Comput. Appl. Math. 234(7), 2175–2182 (2010)

    Article  Google Scholar 

  17. Ghosh, S., Bai, J., Raghavan, P.: Concurrent multi-level model for damage evolution in microstructurally debonding composites. Mech. Mater. 39(3), 241–266 (2007)

    Article  Google Scholar 

  18. Granta Design, Granta CES Selector. https://www.grantadesign.com/products/ces/ 2016. (Accessed 21 June 2016)

  19. Groh, S., Marin, E.B., Horstemeyer, M.F., Zbib, H.M.: Multiscale modeling of the plasticity in an aluminum single crystal. Int. J. Plast. 25(8), 1456–1473 (2009)

    Article  Google Scholar 

  20. Hao, S., Moran, B., Liu, W.K., Olson, G.B.: A hierarchical multi-physics model for design of high toughness steels. J. Computer-Aided Mater. Des. 10, 99–142 (2003)

    Article  Google Scholar 

  21. Hao, S., Liu, W.K., Moran, B., Vernerey, F., Olson, G.B.: Multi-scale constitutive model and computational framework for the design of ultra-high strength, high toughness steels. Comput. Methods Appl. Mech. Eng. 193, 1865–1908 (2004)

    Article  Google Scholar 

  22. Hill, R.: Elastic properties of reinforced solids: some theoretical principles. J. Mech. Phys. Solids. 11, 357–372 (1963)

    Article  Google Scholar 

  23. Holdren, J.P.: National Science and Technology Council, Materials Genome Initiative for Global Competitiveness. http://www.whitehouse.gov/sites/default/files/microsites/ostp/materials_genome_initiative-final.pdf, (2011). (Accessed 21 June 2016)

  24. Holdren, J.P.: National Science and Technology Council, Committee on Technology, Subcommittee on the Materials Genome Initiative, Materials Genome Initiative Strategic Plan. https://www.whitehouse.gov/sites/default/files/microsites/ostp/NSTC/mgi_strategic_plan_-_dec_2014.pdf (2014). (Accessed 21 June 2016)

  25. Horstemeyer, M.F., McDowell, D.L.: Modeling effects of dislocation substructure in polycrystal elastoviscoplasticity. Mech. Mater. 27, 145–163 (1998)

    Article  Google Scholar 

  26. Horstemeyer, M.F.: Integrated Computational Materials Engineering (ICME) for Metals: Using Multiscale Modeling to Invigorate Engineering Design with Science, 1st edn. Wiley, Hoboken (2012)

    Book  Google Scholar 

  27. Hughes, D.A., Hansen, N.: High angle boundaries and orientation distributions at large strains. Scripta Metall. Mater. 33(2), 315–321 (1995)

    Article  Google Scholar 

  28. Hughes, D.A., Liu, Q., Chrzan, D.C., Hansen, N.: Scaling of microstructural parameters: misorientations of deformation induced boundaries. Acta Mater. 45(1), 105–112 (1997)

    Article  Google Scholar 

  29. Hull, D., Bacon, D.J.: Introduction to Dislocations, 5th edn. ButterworthHeinemann, Oxford (2011)

    Google Scholar 

  30. Kalidindi, S.R., Houskamp, J.R., Lyon, M., Adams, B.L.: Microstructure sensitive design of an orthotropic plate subjected to tensile load. Int. J. Plast. 20(8–9), 1561–1575 (2004)

    Article  Google Scholar 

  31. Kalidindi, S.R., Houskamp, J., Proust, G., Duvvuru, H.: Microstructure sensitive design with first order homogenization theories and finite element codes. Mater. Sci. Forum, v 495–497, n PART 1, Textures of Materials, ICOTOM 14 – Proc. 14th Int. Conf. on Textures of Materials, 23–30 (2005)

    Google Scholar 

  32. Kalidindi, S.R., Niezgoda, S.R., Landi, G., Fast, T.: A novel framework for building materials knowledge systems. Comput. Mater. Contin. 17(2), 103–125 (2010)

    Google Scholar 

  33. Kalidindi, S.R., Niezgoda, S.R., Salem, A.A.: Microstructure informatics using higher-order statistics and efficient data-mining protocols. JOM. 63(4), 34–41 (2011)

    Article  Google Scholar 

  34. Kalidindi, S.R.: Hierarchical Materials Informatics, 1st edn. Butterworth-Heinemann, Oxford (2015)

    Google Scholar 

  35. Knezevic, M., Kalidindi, S.R., Mishra, R.K.: Delineation of first-order closures for plastic properties requiring explicit consideration of strain hardening and crystallographic texture evolution. Int. J. Plast. 24(2), 327–342 (2008)

    Article  Google Scholar 

  36. Kocks, U.F.: The relation between polycrystal deformation and single-crystal deformation. Metall. Trans. A. 1, 1121–1143 (1970)

    Article  Google Scholar 

  37. Kristensen, J., Zabaras, N.: Bayesian uncertainty quantification in the evaluation of alloy properties with the cluster expansion method. Comput. Phys. Commun. 185, 2885–2892 (2014)

    Article  Google Scholar 

  38. Kuhlmann-Wilsdorf, D.: Theory of plastic deformation: properties of low energy dislocation structures. Mater. Sci. Eng. A. 113, 1–41 (1989)

    Article  Google Scholar 

  39. Landi, G., Niezgoda, S.R., Kalidindi, S.R.: Multi-scale modeling of elastic response of three-dimensional voxel-based microstructure datasets using novel DFT-based knowledge systems. Acta Mater. 58(7), 2716–2725 (2010)

    Article  Google Scholar 

  40. Lebensohn, R.A., Liu, Y., Ponte Castañeda, P.: Macroscopic properties and field fluctuations in model power-law polycrystals: full-field solutions versus self-consistent estimates. Proc. R. Soc. Lond. A. 460, 1381–1405 (2004)

    Article  Google Scholar 

  41. Lebensohn, R.A., Kanjarla, K.A., Eisenlohr, P.: An elasto-viscoplastic formulation based on fast Fourier transforms for the prediction of micromechanical fields in polycrystalline materials. Int. J. Plast. 32–33, 59–69 (2012)

    Article  Google Scholar 

  42. Leffers, T.: Lattice rotations during plastic deformation with grain subdivision. Mater. Sci. Forum. 157–162, 1815–1820 (1994)

    Article  Google Scholar 

  43. Li, C., Mahadevan, S.: Role of calibration, validation, and relevance in multi-level uncertainty integration. Reliab. Eng. Syst. Saf. 148, 32–43 (2016)

    Article  Google Scholar 

  44. Liu, W.K., Park, H.S., Qian, D., Karpov, E.G., Kadowaki, H., Wagner, G.J.: Bridging scale methods for nanomechanics and materials. Comput. Methods Appl. Mech. Eng. 195, 1407–1421 (2006)

    Article  Google Scholar 

  45. Liu, W.K., Qian, D., Gonella, S., Li, S., Chen, W., Chirputkar, S.: Multiscale methods for mechanical science of complex materials: bridging from quantum to stochastic multiresolution continuum. Int. J. Numer. Methods. Eng. 83, 1039–1080 (2010)

    Article  Google Scholar 

  46. Lyon, M., Adams, B.L.: Gradient-based non-linear microstructure design. J. Mech. Phys. Solids. 52(11), 2569–2586 (2004)

    Article  Google Scholar 

  47. Matthews, J., Klatt, T., Morris, C., Seepersad, C.C., Haberman, M., Shahan, D.: Hierarchical design of negative stiffness metamaterials using a Bayesian network classifier. J. Mech. Des. 138, 041404 (2016)

    Article  Google Scholar 

  48. McDowell, D.L.: Evolving structure and internal state variables. Nadai Award Lecture, ASME Materials Division, ASME IMECE, Dallas (1997)

    Google Scholar 

  49. McDowell, D.L.: Non-associative aspects of multiscale evolutionary phenomena. Proc. 4th International Conference on Constitutive Laws for Engineering Materials, eds. R.C. Picu and E. Krempl:54–57 (1999)

    Google Scholar 

  50. McDowell, D.L.: Materials design: a useful research focus for inelastic behavior of structural metals. Sih, G.C., Panin, V.E. (eds.) Special Issue of the Theoretical and Applied Fracture Mechanics, Prospects of Mesomechanics in the 21st Century: Current Thinking on Multiscale Mechanics Problems, 37:245–259 (2001)

    Google Scholar 

  51. McDowell, D.L., Gall, K., Horstemeyer, M.F., Fan, J.: Microstructure-based fatigue modeling of cast A356-T6 alloy. Eng. Fract. Mech. 70(1), 49–80 (2003)

    Article  Google Scholar 

  52. McDowell, D.L.: Simulation-assisted materials design for the concurrent design of materials and products. JOM. 59(9), 21–25 (2007)

    Article  Google Scholar 

  53. McDowell, D.L., Olson, G.B.: Concurrent design of hierarchical materials and structures. Sci. Model. Simul. (CMNS). 15(1), 207 (2008)

    Article  Google Scholar 

  54. McDowell, D.L.: Viscoplasticity of heterogeneous metallic materials. Mater. Sci. Eng. R. Rep. 62(3), 67–123 (2008)

    Article  Google Scholar 

  55. McDowell, D.L.: A perspective on trends in multiscale plasticity. Int. J. Plast. 26(9), 1280–1309 (2010)

    Article  Google Scholar 

  56. McDowell, D.L., Backman, D.: Simulation-assisted design and accelerated insertion of materials. Ch. 19. In: Ghosh, S., Dimiduk, D. (eds.) Computational Methods for Microstructure-Property Relationships, Springer, ISBN 978–1–4419-0642-7 (2010)

    Google Scholar 

  57. McDowell, D.L., Dunne, F.P.E.: Microstructure-sensitive computational modeling of fatigue crack formation. Int. J. Fatigue. 32(9), 1521–1542 (2010)

    Article  Google Scholar 

  58. McDowell, D.L., Panchal, J.H., Choi, H.-J., Seepersad, C.C., Allen, J.K., Mistree, F.: Integrated Design of Multiscale, Multifunctional Materials and Products, 1st edn. Butterworth-Heinemann, Elsevier Inc., ISBN-13: 978–1–85617-662-0 (2010)

    Google Scholar 

  59. McDowell, D.L., Ghosh, S., Kalidindi, S.R.: Representation and computational structure-property relations of random media. JOM. 63(3), 45–51 (2011)

    Article  Google Scholar 

  60. McDowell, D.L., Kalidindi, S.R.: The materials innovation ecosystem: a key enabler for the materials genome initiative. MRS Bull. 41, 326–335 (2016)

    Article  Google Scholar 

  61. McDowell, D.L., LeSar, R.A.: The need for microstructure informatics in process-structure-property relations. MRS Bull. 41, 587–593 (2016)

    Article  Google Scholar 

  62. Moody, N.R., Foiles, S.M.: An atomistic study of hydrogen effects on the fracture of tilt boundaries in nickel. MRS Proc. 238, 381 (1992). https://doi.org/10.1557/PROC-238-381

    Article  Google Scholar 

  63. Mullins, J., Mahadevan, S.: Bayesian uncertainty integration for model calibration, validation, and prediction. J. Verification Validation Uncertain. Quantif. 1(1), 011006 (2016)

    Article  Google Scholar 

  64. Mura, T.: Micromechanics of Defects in Solids, 2nd edn. Kluwer Academic Publishers, The Netherlands (1987)

    Book  Google Scholar 

  65. Narayanan, S., McDowell, D.L., Zhu, T.: Crystal plasticity model for BCC iron atomistically informed by kinetics of correlated kinkpair nucleation on screw dislocations. J. Mech. Phys. Solids. 65, 54–68 (2014)

    Article  Google Scholar 

  66. Olson, G.B.: Computational design of hierarchically structured materials. Science. 277(5330), 1237–1242 (1997)

    Article  Google Scholar 

  67. Olson, G.B.: Designing a new material world. Science. 288, 993–998 (2000)

    Article  Google Scholar 

  68. Ostoja-Starzewski, M.: Scale effects in plasticity of random media: status and challenges. Int. J. Plast. 21, 1119–1160 (2005)

    Article  Google Scholar 

  69. Ozdemir, I., Brekelmans, W.A.M., Geers, M.G.D.: Modeling thermal shock damage in refractory materials via direct numerical simulation (DNS). J. Eur. Ceram. Soc. 30(7), 1585–1597 (2010)

    Article  Google Scholar 

  70. Panchal, J.H., Choi, H.-J., Shepherd, J., Allen, J.K., McDowell, D.L., Mistree, F.: A strategy for simulation-based multiscale, multifunctional design of products and design processes. ASME Design Automation Conference, Long Beach, CA. Paper Number: DETC2005–85316 (2005)

    Google Scholar 

  71. Panchal, J.H., Kalidindi, S.R., McDowell, D.L.: Key computational modeling issues in ICME. Comput. Aided Des. 45(1), 4–25 (2013)

    Article  Google Scholar 

  72. Panchal, J.H.: A framework for simulation-based integrated design of multiscale products and design processes. PhD Dissertation, G.W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta (2005)

    Google Scholar 

  73. Pollock, P.M., Allison, J.E.: Integrated Computational Materials Engineering: A Transformational Discipline for Improved Competitiveness and National Security. Committee on Integrated Computational Materials Engineering, National Materials Advisory Board, Division of Engineering and Physical Sciences, National Research Council of the National Academies, National Academies Press, Washington, DC (2008)

    Google Scholar 

  74. Prakash, A., Lebensohn, R.A.: Simulation of micromechanical behavior of polycrystals: finite elements vs. fast Fourier transforms. Model. Simul. Mater. Sci. Eng. 17, 064010 (2009)

    Article  Google Scholar 

  75. Qu, J., Cherkaoui, M.: Fundamentals of Micromechanics in Solids. Wiley, Hoboken (2006.) ISBN 978-0-471-46451-8

    Book  Google Scholar 

  76. Rajan, K.: Learning from systems biology: an “omics” approach to materials design. JOM. 60(3), 53–55 (2008)

    Article  Google Scholar 

  77. Rajan, K.: Informatics for Materials Science and Engineering, 1st edn. Butterworth-Heinemann, Oxford (2013)

    Google Scholar 

  78. Rice, J.R., Thomson, R.: Ductile versus brittle behavior of crystals. Philos. Mag. 29(1), 73 (1974)

    Article  Google Scholar 

  79. Rice, J.R., Wang, J.-S.: Embrittlement of interfaces by solute segregation. Mater. Sci. Eng. A107, 23–40 (1989)

    Article  Google Scholar 

  80. Sankaran, S., Zabaras, N.: Computing property variability of polycrystals induced by grain size and orientation uncertainties. Acta Mater. 55(7), 2279–2290 (2007)

    Article  Google Scholar 

  81. Seepersad, C.C.: A robust topological preliminary design exploration method with materials design applications. PhD Dissertation, G.W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta (2004)

    Google Scholar 

  82. Seepersad, C.C., Fernandez, M.G., Panchal, J.H., Choi, H.J., Allen, J.K., McDowell, D.L., Mistree, F.: Foundations for a systems-based approach for materials design. 10th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference. Albany: AIAA MAO, AIAA-2004-4300 (2004)

    Google Scholar 

  83. Seepersad, C.C.: Challenges and opportunities in design for additive manufacturing. 3D Print. Addit. Manuf. 1(1), 10–13 (2014)

    Article  Google Scholar 

  84. Shahan, D., Seepersad, C.C.: Bayesian network classifiers for set-based collaborative design. J. Mech. Des. 134(7), 071001 (2012)

    Article  Google Scholar 

  85. Shenoy, M.M., Zhang, J., McDowell, D.L.: Estimating fatigue sensitivity to polycrystalline Ni-base superalloy microstructures using a computational approach. Fatigue Fract. Eng. Mater. Struct. 30(10), 889–904 (2007)

    Article  Google Scholar 

  86. Shenoy, V.B., Miller, R., Tadmor, E.B., Phillips, R., Ortiz, M.: Quasicontinuum models of interfacial structure and deformation. Phys. Rev. Lett. 80(4), 742–745 (1998)

    Article  Google Scholar 

  87. Shenoy, V.B., Miller, R., Tadmor, E., Rodney, D., Phillips, R., Ortiz, M.: An adaptive finite element approach to atomic-scale mechanics – the quasicontinuum method. J. Mech. Phys. Solids. 47(3), 611–642 (1999)

    Article  Google Scholar 

  88. Shu, C., Rajagopalan, A., Ki, X., Rajan, K.: Combinatorial materials design through database science. Mat. Res. Soc. Symp. – Proc., v 804, Combinatorial and Artificial Intelligence Methods in Materials Science II:333–341 (2003)

    Google Scholar 

  89. Suquet, P.M.: Homogenization Techniques for Composite Media Lecture Notes in Physics, vol. 272. Springer, Berlin (1987)

    Google Scholar 

  90. Tadmor, E.B., Ortiz, M., Phillips, R.: Quasicontinuum analysis of defects in solids. Philos. Mag. A. 73(6), 1529–1563 (1996a)

    Article  Google Scholar 

  91. Tadmor, E.B., Phillips, R., Ortiz, M.: Mixed atomistic and continuum models of deformation in solids. Langmuir. 12(19), 4529–4534 (1996b)

    Article  Google Scholar 

  92. Taguchi, G.: Taguchi on Robust Technology Development: Bringing Quality Engineering Upstream. ASME Press, New York (1993)

    Book  Google Scholar 

  93. Vernerey, F., Liu, W.K., Moran, B.: Multi-scale micromorphic theory for hierarchical materials. J. Mech. Phys. Solids. 55, 2603–2651 (2007)

    Article  Google Scholar 

  94. Zohdi, T.I.: Constrained inverse formulations in random material design. Comput. Methods Appl. Mech. Eng. 192(28–30), 3179–3194 (2003)

    Article  Google Scholar 

Download references

Acknowledgments

The author is grateful for the support of the Carter N. Paden, Jr. Distinguished Chair in Metals Processing at Georgia Tech, as well as prior support of AFOSR, ONR D3D, Eglin AFB, DARPA, NAVAIR, QuesTek, the NSF-funded PSU-GT Center for Computational Materials Design, SIMULIA, NSF CMMI-1232878, NSF CMMI-0758265, and NSF CMMI-1030103.

Author information

Authors and Affiliations

  1. Woodruff School of Mechanical Engineering, School of Materials Science and Engineering, Georgia Institute of Technology, Atlanta, GA, 30332-0620, USA

    David L. McDowell

Authors
  1. David L. McDowell
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to David L. McDowell .

Editor information

Editors and Affiliations

  1. Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA

    Dongwon Shin

  2. QuesTek Innovations, LLC, Evanston, Illinois, USA

    James Saal

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

McDowell, D.L. (2018). Microstructure-Sensitive Computational Structure-Property Relations in Materials Design. In: Shin, D., Saal, J. (eds) Computational Materials System Design. Springer, Cham. https://doi.org/10.1007/978-3-319-68280-8_1

Download citation

Publish with us

Policies and ethics

Search

Navigation