JOM

, Volume 61, Issue 5, pp 19–23

Toward a virtual platform for materials processing

Coupled Models of Materials Processes Overview

Abstract

Any production is based on materials eventually becoming components of a final product. Material properties being determined by the microstructure of the material thus are of utmost importance both for productivity and reliability of processing during production and for application and reliability of the product components. A sound prediction of materials properties therefore is highly important. Such a prediction requires tracking of microstructure and properties evolution along the entire component life cycle starting from a homogeneous, isotropic and stress-free melt and eventually ending in failure under operational load. This article will outline ongoing activities at the RWTH Aachen University aiming at establishing a virtual platform for materials processing comprising a virtual, integrative numerical description of processes and of the microstructure evolution along the entire production chain and even extending further toward microstructure and properties evolution under operational conditions.

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References

  1. 1.
    T. Zacharia et al., “Modeling of Fundamental Phenomena in Welds,” Modelling and Simulation in Materials Science and Engineering, 3 (1995), pp. 265–288.CrossRefADSGoogle Scholar
  2. 2.
    T. Belytschko and T. Black, “Elastic Crack Growth in Finite Elements with Minimal Remeshing,” International Journal for Numerical Methods in Engineering, 45(5) (1999), pp. 601–620.MATHCrossRefMathSciNetGoogle Scholar
  3. 3.
    R. Kobayashi, “Modeling and Numerical Simulations of Dendritic Crystal Growth,” Physica D, 63(3–4) (1993), pp. 410–423.MATHCrossRefADSGoogle Scholar
  4. 4.
    I. Steinbach et al., “A Phase Field Concept for Multiphase Systems,” Physica D, 94 (1996), pp. 135–147.MATHCrossRefGoogle Scholar
  5. 5.
    K. Terada and N. Kikuchi, “A Class of General Algorithms for Multi-Scale Analyses of Heterogeneous Media,” Computer Methods in Applied Mechanics and Engineering, 190(40–41) (2001) pp. 5427–5464.MATHCrossRefMathSciNetGoogle Scholar
  6. 6.
    www.calphad.org/.Google Scholar
  7. 7.
    www.thermocalc.se.Google Scholar
  8. 8.
    www.thermotech.co.uk/jmatpro.html.Google Scholar
  9. 9.
    www.computherm.com/pandat.html.Google Scholar
  10. 10.
    MICRESS®—The MICRostructure Evolution Simulation Software, www.micress.de.Google Scholar
  11. 11.
    Integrated Computational Materials Engineering: A Transformational Discipline for Improved Competitiveness and National Security (Washington, D.C.: National Academic Press, 2008), ISBN: 0-309-12000-4, www.wpi.edu/Images/CMS/MPI-Members/NMAB_ICME_Report.pdf.Google Scholar
  12. 12.
    TMS Forum on ICME: iweb.tms.org/forum/.Google Scholar
  13. 13.
    D. Apelian, “Integrated Computational Materials Engineering (ICME): A “Model” for the Future?” JOM, 60(7) (2008), p. 9.CrossRefGoogle Scholar
  14. 14.
    G. Gottstein, editor, Integral Materials Modelling: Towards Physics Based Through-Process Models (Weinheim, Germany: Wiley-VCH Verlag, 2007), ISBN 978-3-527-31711-0.Google Scholar
  15. 15.
    Jürgen Hirsch, “Through Process Modelling,” Materials Science Forum, 519–521 (2006), pp. 15–24.CrossRefGoogle Scholar
  16. 16.
    Immpetus Sheffield, “A New Framework for Hybrid Through-Process Modelling, Process Simulation and Optimisation in the Metals Industry,” www.immpetus.group.shef.ac.uk/.Google Scholar
  17. 17.
    Hierarchical Engineering of Industrial Materials (hero-m), Stockholm, Sweden, www.hero-m.mse.kth.se, activity started in 2007 at KTH Stockholm.Google Scholar
  18. 18.
    Three years activity on “Through-Process-Modelling of Al-alloys” started in spring 2008 at the Institute for Virtual Production at ETH-Zurich.Google Scholar
  19. 19.
    P. Li et al., “A Through Process Model of the Impact of In-service Loading, Residual Stress, and Microstructure on the Final Fatigue Life of an A356 Automotive Wheel,” Materials Science and Engineering A, 460–461 (2007), pp. 20–30.CrossRefGoogle Scholar
  20. 20.
    www.icams.de.Google Scholar
  21. 21.
    Will Schroeder, Ken Martin, and Bill Lorensen, The Visualization Toolkit-An Object-Oriented Approach to 3D Graphics, 4th Edition (Kitware, Inc., 28 Corporate Drive, Clifton Park, NY 12065, 2006), www.vtk.org.Google Scholar
  22. 22.
    D. Schilberg, A. Gramatke, and K. Henning, “Semantic Interconnection of Distributed Numerical Simulations via SOA,” Proc. of the World Congress on Engineering and Computer Science (Engineering Letters, Unit 1, 1/F, 37-39 Hung to Road, Hong Kong, 2008), ISBN 978-988-98671-0-2, pp. 894–897.Google Scholar
  23. 23.
    D. Thain, T. Tannenbaum, and M. Livny, “Distributed Computing in Practice: The Condor Experience,” Concurrency and Computation: Practice and Experience, 17(2–4) (2005), pp. 323–356, www.cs.wisc.edu/condor.CrossRefGoogle Scholar
  24. 24.
    P. Cerfontaine et al., “Towards a Flexible and Distributed Simulation Platform,” Chapter 1 Computational Science and Its Applications-ICCSA 2008 (Heidelberg, Germany, Springer, 2008), pp. 867–882.Google Scholar
  25. 25.
    S. Benke, “A Multi-Phase-Field Model including Inelastic Deformation for Solid State Transformations,” Proceedings in Applied Mathematics and Mechanics, 8(1) (2008), pp. 10407–10408CrossRefGoogle Scholar
  26. 26.
    N. Bakhvalov and G. Panasenko, Homogenisation: Averaging Processes in Periodic Media (Dordrecht, the Netherlands: Kluwer, 1989).MATHGoogle Scholar
  27. 27.
    J.C. Michel, H. Moulinec, and P. Suquet, “Effective Properties of Composite Materials with Periodic Microstructure: A Computational Approach,” Comput. Meth. Appl. Mech. Engng., 172 (1999), p. 109.MATHCrossRefMathSciNetGoogle Scholar
  28. 28.
    Q. Yu and J. Fish, “Temporal Homogenization of Viscoelastic and Viscoplastic Solids Subjected to Locally Periodic Loading,” Computational Mechanics, 29(3) (2002), pp. 199–211.CrossRefADSMathSciNetGoogle Scholar
  29. 29.
    G. Laschet et al., “Effective Anisotropic Properties of Semi-Crystalline Polypropylene via a Two-Level Homogenization Scheme” (Paper presented at the Second GAMM-Seminar on Multiscale Material Modelling, Stuttgart, Germany, 11–12 July 2008).Google Scholar
  30. 30.
    K. Bobzin et al., “Microstructure Dependency of the Material Properties: Simulation Approaches and Calculation Methods,” Steel Research Int., 78(10–11) (2007), pp. 804–811.Google Scholar
  31. 31.
    V. Uthaisangsuk, U. Prahl, and W. Bleck, “Stretch-Flangeability Characterization of Multiphase Steel using a Microstructure Based Failure Modelling,” J. Comp. Mat. Sci., 45 (2009), pp. 617–623.CrossRefGoogle Scholar
  32. 32.
    C. Thomser et al., “Modelling the Mechanical Properties of Multiphase Steels,” Computer Methods in Materials Science, 7(1) (2007), pp. 42–46.Google Scholar
  33. 33.
    M. Apel, S. Benke, and I. Steinbach, “Virtual Dilatometer Curves and Effective Young’s Modulus of a 3-D Multiphase Structure Calculated by the Phase-Field Method,” J. Comp. Mat. Sci., 45 (2009), pp. 589–592CrossRefGoogle Scholar
  34. 34.
    J. Gedicke et al., “Weld Depth Control in Fiber Laser Welding of Thin Metal Sheets” (Paper presented at the 27th International Congress on Applications of Lasers & Electro-Optics-ICALEO, Temecula, CA, October 2008).Google Scholar
  35. 35.
    W. Michaeli et al., “Integrative Materials Modelling of Semi-Crystalline Thermoplastic Parts” (Paper presented at the 24th Annual Meeting of the Polymer Processing Society (PPS), Salerno, Italy, 15–19 June 2008).Google Scholar
  36. 36.
    S. Benke et al., “Modeling Hot Rolling: A Study on the Microstructural Changes during the Austenite to Ferrite Phase Transformation in Dual Phase Steels” (Paper presented at the 8th World Congress on Computational Mechanics (WCCM8)-5th European Congress on Computational Methods in Applied Sciences and Engineering (ECCOMAS 2008), Venice, Italy, June 30–July 5, 2008).Google Scholar
  37. 37.
    E. Rossiter and O. Mokrov, “Integration of the Simulation Package SimWeld into FEM Analyzers for the Modelling of Welding Processes” (Paper presented at the Sysweld Forum 2007, Weimar, Germany, 15 November 2007).Google Scholar
  38. 38.
    RWTH University Aachen House of Production, Steinbachstrasse 53, Aachen D-52074, Germany, www.production-research.de.Google Scholar

Copyright information

© TMS 2009

Authors and Affiliations

  1. 1.RWTH Aachen UniversityAachenGermany
  2. 2.Department of Ferrous MetallurgyRWTH Aachen UniversityAachenGermany

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