Advertisement

Data Structures and Workflows for ICME

  • Sean P. DoneganEmail author
  • Michael A. Groeber
Chapter
  • 67 Downloads

Abstract

Integrated computational materials engineering (ICME) represents a grand challenge within materials research and development. Effective ICME involves coupling materials characterization and experimentation with simulation tools to produce a holistic understanding of the materials system, promising to accelerate the materials development enterprise. Under the Center of Excellence on Integrated Materials Modeling (CEIMM), significant strides were made in developing state-of-the-art experimental methods and simulation techniques for interrogating material structure and behavior across multiple scales. In parallel to these method developments, several advances were made in designing data structures and workflow tools that possess the required flexibility and extensibility to operate on the data produced by such advanced methods. Such software tools are a critical enabling component for effective ICME; the National Academy of Sciences noted cyberinfrastructure as a crucial factor for ICME, to include databases, software, and computational hardware [1]. Additionally, these tools enable workflows that properly integrate models and experimentation at each stage of the materials development lifecycle. Figure 1 schematically shows such a workflow for optimization of microstructure and properties in a titanium forging.

Keywords

Integrated computational materials engineering Data structures Computer science Image processing Data fusion Machine learning Multiscale materials modeling Software engineering Open source software Data visualization 

Notes

Acknowledgments

The authors would like to acknowledge Mike Jackson, for his vision and programming expertise in enabling the implementation of the SIMPL architecture; Dennis Dimiduk, for his consistent support and fruitful discussions; Adam Pilchak, for motivating the demonstrated ICME use case and providing the data and material; Mike Uchic, for providing characterization support and contribution to the vision of SIMPL; and Chris Woodward, for his unyielding support in the early stages of designing DREAM.3D.

References

  1. 1.
    National Research Council, Integrated Computational Materials Engineering: A Transformational Discipline for Improved Competitiveness and National Security (The National Academies Press, Washington, DC, 2008).  https://doi.org/10.17226/12199. CrossRefGoogle Scholar
  2. 2.
    B. Puchala, G. Tarcea, E.A. Marquis, M. Hedstrom, H.V. Jagadish, J.E. Allison, The materials commons: a collaborative platform and information repository for the global materials community. JOM 68(8), 2035–2044 (2016).  https://doi.org/10.1007/s11837-016-1998-7 CrossRefGoogle Scholar
  3. 3.
    B. Blaiszik, K. Chard, J. Pruyne, R. Ananthakrishnan, S. Tuecke, I. Foster, The materials data facility: data services to advance materials science research. JOM 68(8), 2045–2052 (2016).  https://doi.org/10.1007/s11837-016-2001-3 CrossRefGoogle Scholar
  4. 4.
    A. Jain, S.P. Ong, G. Hautier, W. Chen, W.D. Richards, S. Dacek, S. Cholia, D. Gunter, D. Skinner, G. Ceder, K.A. Persson, Commentary: the materials project: a materials genome approach to accelerating materials innovation. APL Mater 1(1) (2013).  https://doi.org/10.1063/1.4812323
  5. 5.
    J. Ahrens, B. Gevecki, C. Law, ParaView: an end-user tool for large data visualization, in Visualization Handbook, (Elsevier, Amsterdam, 2005)Google Scholar
  6. 6.
    Applications. (Scientific Forming Technologies Corporation) [Online]. Available: https://www.deform.com/applications/. Accessed Mar 2019
  7. 7.
    Casting Applications. (ESI Group) [Online]. Available: https://www.esi-group.com/software-solutions/virtual-manufacturing/casting/applications. Accessed Mar 2019
  8. 8.
    Abaqus Unified FEA. (Dassault Systemes) [Online]. Available: https://www.3ds.com/products-services/simulia/products/abaqus/. Accessed Mar 2019
  9. 9.
    ANSYS. (ANSYS) [Online]. Available: https://www.ansys.com/. Accessed Mar 2019
  10. 10.
    Albany. (Sandia National Laboratories, Center for Computing Research) [Online]. Available: https://cfwebprod.sandia.gov/cfdocs/CompResearch/templates/insert/project.cfm?proj=28. Accessed Mar 2019
  11. 11.
    A.G. Salinger, R.A. Bartlett, A.M. Bradley, Q. Chen, I.P. Demeshko, X. Gao, G.A. Hansen, A. Mota, R.P. Muller, E. Nielsen, J.T. Ostien, R.P. Pawlowski, M. Perego, E.T. Phipps, W. Sun, I.K. Tezaur, Albany: using component-based design to develop a flexible, generic multiphysics analysis code. Int J Multiscale Comput Eng 14(4), 415–438 (2016).  https://doi.org/10.1615/IntJMultCompEng.2016017040 CrossRefGoogle Scholar
  12. 12.
    MOOSE: Multiphysics Object Oriented Simulation Environment. (Idaho National Laboratory) [Online]. Available: https://mooseframework.org/. Accessed Mar 2019
  13. 13.
    D. Gaston, C. Newman, G. Hansen, D. Lebrun-Grandie, MOOSE: A parallel computational framework for coupled systems of nonlinear equations. Nucl. Eng. Des. 239(10), 1768–1778 (2009)CrossRefGoogle Scholar
  14. 14.
    H. Moulinec, P. Suquet, A numerical method for computing the overall response of nonlinear composites with complex microstructure. Comput. Methods Appl. Mech. Eng. 157(1–2), 69–94 (1998).  https://doi.org/10.1016/S0045-7825(97)00218-1 CrossRefGoogle Scholar
  15. 15.
    J.C. Michel, H. Moulinec, P. Suquet, A computational scheme for linear and non-linear composites with arbitrary phase contrast. Numer Methods Eng 52(1–2), 139–160 (2001).  https://doi.org/10.1002/nme.275 CrossRefGoogle Scholar
  16. 16.
    S.P. Donegan, A.D. Rollett, Simulation of residual stress and elastic energy density in thermal barrier coatings using fast Fourier transforms. Acta Mater. 96, 212–228 (2015).  https://doi.org/10.1016/j.actamat.2015.06.019 CrossRefGoogle Scholar
  17. 17.
    R. A. Lebensohn, N-site modeling of a 3D viscoplastic polycrystal using Fast Fourier Transform. Acta Mater. 49(14), 2723–2737 (2001).  https://doi.org/10.1016/S1359-6454(01)00172-0 CrossRefGoogle Scholar
  18. 18.
    P. Eisenlohr, M. Diehl, R.A. Lebensohn, F. Roters, A spectral method solution to crystal elasto-viscoplasticity at finite strains. Int. J. Plast. 46, 37–53 (2013).  https://doi.org/10.1016/j.ijplas.2012.09.012 CrossRefGoogle Scholar
  19. 19.
    F. Roters, M. Diehl, P. Shanthraj, P. Eisenlohr, C. Reuber, S.L. Wong, T. Maiti, A. Ebrahimi, T. Hochrainer, H.-O. Fabritius, S. Nikolov, M. Friak, N. Fujita, N. Grilli, K.G.F. Janssens, N. Jia, P.J.J. Kok, D. Ma, F. Meiner, E. Werner, M. Stricker, D. Weygand, D. Raabe, DAMASK – The Dusseldorf advanced material simulation kit for modeling multi-physics crystal plasticity, thermal, and damage phenomena from the single crystal up to the component scale. Comput. Mater. Sci. 158, 420–478 (2019)CrossRefGoogle Scholar
  20. 20.
    What is VASP? (VASP Software GmbH) [Online]. Available: https://www.vasp.at/index.php/about-vasp/59-about-vasp. Accessed Mar 2019
  21. 21.
    LAMMPS Molecular Dynamics Simulator. (Sandia National Laboratories) [Online]. Available: https://lammps.sandia.gov/. Accessed Mar 2019
  22. 22.
    ParaDiS. (Lawrence Livermore National Laboratory) [Online]. Available: http://paradis.stanford.edu/site/home. Accessed Mar 2019
  23. 23.
  24. 24.
    GeoDict – The Digital Material Laboratory. (Math2Market GmbH) [Online]. Available: https://www.math2market.com/Solutions/aboutGD.php. Accessed Mar 2019
  25. 25.
    ESPRIT QUBE – Advanced 3D analysis of EBSD/EDS Data. (Bruker Corporation) [Online]. Available: https://www.bruker.com/products/x-ray-diffraction-and-elemental-analysis/eds-wds-ebsd-sem-micro-xrf-and-sem-micro-ct/quantax-ebsd/esprit-qube.html. Accessed Mar 2019
  26. 26.
    D. Wheeler, D. Brough, T. Fast, S. Kalidindi, A. Reid, PyMKS: Materials Knowledge System in Python (2014).  https://doi.org/10.6084/m9.figshare.1015761 CrossRefGoogle Scholar
  27. 27.
    S.R. Niezgoda, D.T. Fullwood, S.R. Kalidindi, Delineation of the space of 2-point correlations in a composite material system. Acta Mater. 56(18), 5285–5292 (2008).  https://doi.org/10.1016/j.actamat.2008.07.005 CrossRefGoogle Scholar
  28. 28.
    T. Fast, S.R. Kalidindi, Formulation and calibration of higher-order elastic localization relationships using the MKS approach. Acta Mater. 59(11), 4595–4605 (2011)CrossRefGoogle Scholar
  29. 29.
    Magpie. (Wolverton Research Group) [Online]. Available: https://bitbucket.org/wolverton/magpie. Accessed Mar 2019
  30. 30.
    MTEX Toolbox [Online]. Available: http://mtex-toolbox.github.io/. Accessed Mar 2019
  31. 31.
    F. Bachmann, R. Hielscher, H. Schaeben, Texture analysis with MTEX – Free and open source software toolbox. Solid State Phenom. 160, 63–68 (2010)CrossRefGoogle Scholar
  32. 32.
    J.M. Sosa, D.E. Huber, B. Welk, H.L. Fraser, Development and application of MIPAR: A novel software package for two- and three-dimensional microstructural characterization. Integr Mater Manuf Innov 3(10), 123 (2014).  https://doi.org/10.1186/2193-9772-3-10 CrossRefGoogle Scholar
  33. 33.
    M.A. Groeber, M.A. Jackson, DREAM.3D: A digital representation environment for the analysis of microstructure in 3D. Integr Mater Manuf Innov 3(5), 56 (2014).  https://doi.org/10.1186/2193-9772-3-5 CrossRefGoogle Scholar
  34. 34.
    ImageJ: Image Processing and Analysis in Java. (National Institutes of Health) [Online]. Available: https://imagej.nih.gov/ij/index.html. Accessed Mar 2019
  35. 35.
    C.A. Schneider, W.S. Rasband, K.W. Eliceri, NIH image to ImageJ: 25 years of image analysis. Nat. Methods 9, 671–675 (2012).  https://doi.org/10.1038/nmeth.2089 CrossRefGoogle Scholar
  36. 36.
    Fiji [Online]. Available: https://fiji.sc/. Accessed Mar 2019
  37. 37.
    J. Schindelin, I. Arganda-Carreras, E. Frise, V. Kaynig, M. Longair, T. Pietzsch, S. Preibisch, C. Rueden, S. Saalfeld, B. Schmid, J.-Y. Tinevez, D.J. White, V. Hartenstein, K. Eliceiri, P. Tomancak, A. Cardona, Fiji: An open-source platform for biological image analysis. Nat. Methods 9, 676–682 (2012).  https://doi.org/10.1038/nmeth.2019 CrossRefGoogle Scholar
  38. 38.
    ITK. (Kitware, Inc.) [Online]. Available: https://itk.org/. Accessed Mar 2019
  39. 39.
    T.S. Yoo, M.J. Ackerman, W.E. Lorensen, W. Schroeder, V. Chalana, S. Aylward, D. Metaxas, R. Whitaker, Engineering and algorithm design for an image processing Api: A technical report on ITK – The insight toolkit. Stud. Health Technol. Inform., 586–592 (2002).  https://doi.org/10.3233/978-1-60750-929-5-586.
  40. 40.
    3D Slicer [Online]. Available: https://www.slicer.org/. Accessed Mar 2019
  41. 41.
    R. Kikinis, S.D. Pieper, K.G. Vosburgh, 3D slicer: A platform for subjet-specific image analysis, visualization, and clinical support, in Intraoperative Imaging and Image-Guided Therapy, (2014), pp. 277–289.  https://doi.org/10.1007/978-1-4614-7657-3_19 CrossRefGoogle Scholar
  42. 42.
    VTK (Kitware, Inc.) [Online]. Available: https://vtk.org/. Accessed Mar 2019
  43. 43.
    W. Schroeder, K. Martin, B. Lorensen, The Visualization Toolkit, 4th edn. (Kitware, 2006)Google Scholar
  44. 44.
    SCIRun. (The NIH/NIGMS Center for Integrative Biomedical Computing) [Online]. Available: http://www.sci.utah.edu/cibc-software/scirun.html. Accessed Mar 2019
  45. 45.
    S.G. Parker, C.R. Johnson, SCIRun: a scientific programming environment for computational steering, in Proceedings of the 1995 ACM/IEEE Conference on Supercomputing, San Diego, 1995.  https://doi.org/10.1109/SUPERC.1995.241689
  46. 46.
    Orange – Data Mining Fruitful & Fun. (University of Ljubljana) [Online]. Available: https://orange.biolab.si/. Accessed Mar 2019
  47. 47.
    J. Demsar, T. Curk, A. Erjavec, C. Gorup, T. Hocevar, M. Milutinovic, M. Mozina, M. Polajnar, M. Toplak, A. Staric, M. Stajdohar, L. Umek, L. Zagar, J. Zbontar, M. Zitnik, B. Zupan, Orange: data mining toolbox in Python. J. Mach. Learn. Res. 14, 2349–2353 (2013)Google Scholar
  48. 48.
    Weka 3: Data Mining Software in Java. (University of Waikato) [Online]. Available: https://www.cs.waikato.ac.nz/ml/weka/index.html. Accessed Mar 2019
  49. 49.
    E. Frank, M.A. Hall, I.H. Witten, The WEKA workbench, in Data Mining: Practical Machine Learning Tools and Techniques, (Morgan Kaufmann, 2016)Google Scholar
  50. 50.
    SIMPL. (BlueQuartz Software, LLC) [Online]. Available: https://github.com/BlueQuartzSoftware/SIMPL. Accessed Mar 2019
  51. 51.
    DREAM3D. (BlueQuartz Software, LLC) [Online]. Available: https://github.com/BlueQuartzSoftware/DREAM3D. Accessed March 2019
  52. 52.
    SIMPLView. (BlueQuartz Software, LLC) [Online]. Available: https://github.com/BlueQuartzSoftware/SIMPLView. Accessed Mar 2019
  53. 53.
    QT | Cross-platform software development for embedded & desktop [Online]. Available: https://www.qt.io/. Accessed Mar 2019
  54. 54.
    Hierarchical Data Format, version 5. (The HDF Group, 1997–2019) [Online]. Available: http://www.hdfgroup.org/HDF5/
  55. 55.
    G. Guennebaud, B. Jacob, Eigen v3. (2010) [Online]. Available: http://eigen.tuxfamily.org. Accessed March 2019
  56. 56.
    Intel Threading Building Blocks. (Intel Corporation) [Online]. Available: https://www.threadingbuildingblocks.org/. Accessed Mar 2019
  57. 57.
    W. Jakob, J. Rhinelander, D. Moldovan, pybind11 – Seamless operability between C++11 and Python. (2019). [Online]. Available: https://github.com/pybind/pybind11. Accessed Mar 2019
  58. 58.
    CMake. (Kitware, Inc.) [Online]. Available: https://cmake.org/. Accessed Mar 2019
  59. 59.
    Xdmf [Online]. Available: http://xdmf.org/index.php/Main_Page. Accessed Mar 2019
  60. 60.
    A.L. Pilchak, J. Shank, J.C. Tucker, S. Srivatsa, P.N. Fagin, S.L. Semiatin, A dataset for the development, verification, and validation of microstructure-sensitive process models for near-alpha titanium alloys. Integr Mater Manuf Innov 5(14), 259 (2016).  https://doi.org/10.1186/s40192-016-0056-1 CrossRefGoogle Scholar
  61. 61.
    A.P. Woodfield, M.D. Gorman, R.R. Corderman, J.A. Sutliff, B. Yamrom, Effect of Microstructure on Dwell Fatigue Behavior of Ti-6242, in Titanium ’95: Science and Technology, (Birmingham, 1996)Google Scholar
  62. 62.
    A.L. Pilchak, A. Huston, W.J. Porter, D.J. Buchanan, R. John, Growth of small and long fatigue cracks in Ti-6Al-4V subjected to cyclic and dwell fatigue, in Proceedings of the 13th World Conference on Titanium, Warrendale, 2016.Google Scholar
  63. 63.
    A.L. Pilchak, A simple model to account for the rolw of microtexture on fatigue and dwell fatigue lifetimes of titanium alloys. Scr. Mater. 74, 68–71 (2014).  https://doi.org/10.1016/j.scriptamat.2013.10.024 CrossRefGoogle Scholar
  64. 64.
    A.K. Jain, M.N. Murty, P.J. Flynn, Data clustering: A review. ACM Comput. Surv. 31(3), 265–323 (1999).  https://doi.org/10.1145/331499.331504 CrossRefGoogle Scholar
  65. 65.
    L. Kaufman, P.J. Rousseeuw, Clustering by means of medoids, in Proceedings of Statistical Data Analysis Based on the L1 Norm, Neuchatel, 1987Google Scholar
  66. 66.
    A.K. Jain, Data clustering: 50 years beyond K-means. Pattern Recogn. Lett. 31(8), 651–666 (2010).  https://doi.org/10.1016/j.patrec.2009.09.011 CrossRefGoogle Scholar
  67. 67.
    P.J. Rousseeuw, Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math. 20, 53–65 (1987).  https://doi.org/10.1016/0377-0427(87)90125-7 CrossRefGoogle Scholar
  68. 68.
    A.L. Pilchak, A.R. Shiveley, J.S. Tiley, D.L. Ballard, AnyStitch: A tool for combining electron backscatter diffraction data sets. J. Microsc. 244(1), 38–44 (2011).  https://doi.org/10.1111/j.1365-2818.2011.03496.x CrossRefGoogle Scholar
  69. 69.
    S. Preibisch, S. Saalfeld, P. Tomancak, Globally optimzal stitching of tiled 3D microscopic image acquisitions. Bioinformatics 25(11), 1463–1465 (2009).  https://doi.org/10.1093/bioinformatics/btp184 CrossRefGoogle Scholar
  70. 70.
    S. Umeyama, Least-squares estimation of transformation parameters between two point patterns. IEEE Trans Pattern Anal Mach Intell 13, 376–380 (1991).  https://doi.org/10.1109/34.88573 CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Air Force Research Laboratory, Materials and Manufacturing Directorate, Wright-Patterson Air Force BaseDaytonUSA
  2. 2.Department of Integrated Systems EngineeringThe Ohio State UniversityColumbusUSA

Personalised recommendations