Skip to main content
SpringerLink
Log in

Microstructure-Informed Cloud Computing for Interoperability of Materials Databases and Computational Models: Microtextured Regions in Ti Alloys

  • Thematic Section: 2nd International Workshop on Software Solutions for ICME
  • Published:
Integrating Materials and Manufacturing Innovation Aims and scope Submit manuscript

Abstract

With the fast global adoption of the Materials Genome Initiative (MGI), scientists and engineers are faced with the need to conduct sophisticated data analytics on large datasets to extract knowledge that can be used in modeling the behavior of materials. This raises a new problem for materials scientists: how to create and foster interoperability and share developed software tools and generated datasets. A microstructure-informed cloud-based platform (MiCloud™) has been developed that addresses this need, enabling users to easily access and insert microstructure informatics into computational tools that predict performance of engineering products by accounting for microstructural dependencies on manufacturing provenance. The platform extracts information from microstructure data by employing algorithms including signal processing, machine learning, pattern recognition, computer vision, predictive analytics, uncertainty quantification, and data visualization. The interoperability capabilities of MiCloud and its various web-based applications are demonstrated in this case study by analyzing Ti6AlV4 microstructure data via automatic identification of various features of interest and quantifying its characteristics that are used in extracting correlations and causations for the associated mechanical behavior (e.g., yield strength, cold-dwell debit, etc.). The data were recorded by two methods: (1) backscattered electron (BSE) imaging for extracting spatial and morphological information about alpha and beta phases and (2) electron backscatter diffraction (EBSD) for extracting spatial, crystallographic, and morphological information about microtextured regions (MTRs) of the alpha phase. Extracting reliable knowledge from generated information requires data analytics of a large amount of multiscale microstructure data which necessitates the development of efficient algorithms (and the associated software tools) for data recording, analysis, and visualization. The interoperability of these tools and superior effectiveness of the cloud computing approach are validated by featuring several examples of its use in alpha/beta titanium alloys and Ni-based superalloys, reflecting the anticipated computational cost and time savings via the use of web-based applications in implementations of microstructure-informed integrated computational materials engineering (ICME).

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17

Similar content being viewed by others

References

  1. Committee on Integrated Computational Materials Engineering National Materials Advisory Board Division on Engineering and Physical Sciences National Research Council (2008) Integrated Computational Materials Engineering. doi: 10.17226/12199

  2. National Science and Technology Council (2011) Materials Genome Initiative for global competitiveness. Executive Office of the President

  3. Ward CH, Warren JA, Hanisch RJ (2014) Making materials science and engineering data more valuable research products. Integr Mater Manuf Innov 3:1–17. doi:10.1186/s40192-014-0022-8

    Article  Google Scholar 

  4. Jacobsen MD, Fourman JR, Porter KM et al (2016) Creating an integrated collaborative environment for materials research. Integr Mater Manuf Innov 5:12. doi:10.1186/s40192-016-0055-2

    Article  Google Scholar 

  5. University of Michigan (2015) PRISM project. https://wiki.umms.med.umich.edu/display/UMHSHELPDESK/Prism

  6. University of Illinnois T2C2: Timely and trusted curation and coordination. In: 2015. http://t2c2.csl.illinois.edu/

  7. van Dam KK, Carson J, Corrigan A et al. (2012) Velo and REXAN 2014; Integrated data management and high speed analysis for experimental facilities. In: 2012 I.E. 8th Int. Conf. E-Science. IEEE, pp 1–9

  8. Carey NS, Budavári T, Daphalapurkar N, Ramesh KT (2016) Data integration for materials research. Integr Mater Manuf Innov 5:7. doi:10.1186/s40192-016-0049-0

    Article  Google Scholar 

  9. McLennan M, Kennell R (2010) HUBzero: a platform for dissemination and collaboration in computational science and engineering. Comput Sci Eng 12:48–53. doi:10.1109/MCSE.2010.41

    Article  Google Scholar 

  10. Wallack AS (1995) Algorithms and techniques for manufacturing. (Ph.D. thesis, University of California at Berkeley)

  11. Bonte MHA, van den Boogaard AH, Huétink J (2007) A metamodel based optimisation algorithm for metal forming processes. In: Adv Methods Mater. Form. Springer Berlin Heidelberg, Berlin, Heidelberg, pp 55–72

    Chapter  Google Scholar 

  12. Raymond E (1999) The cathedral and the bazaar. Knowledge, Technol Policy 12:23–49. doi:10.1007/s12130-999-1026-0

    Article  Google Scholar 

  13. GitHub I GitHub. https://github.com/. Accessed 1 Jan 2016

  14. Hielscher R, Bachmann F (2017) MTEX-A texture calculation toolbox. http://mtex-toolbox.github.io/. Accessed 1 Jan

  15. Bachmann F, Hielscher R, Schaeben H (2011) Grain detection from 2d and 3d EBSD data—specification of the MTEX algorithm. Ultramicroscopy 111:1720–1733. doi:10.1016/j.ultramic.2011.08.002

    Article  CAS  Google Scholar 

  16. BlueQuartz Software LLC DREAM3D. https://github.com/BlueQuartzSoftware/DREAM3D. Accessed 1 Jan 2017

  17. Groeber M, Jackson M (2014) DREAM.3D: a digital representation environment for the analysis of microstructure in 3D. Integr Mater Manuf Innov 3:5. doi:10.1186/2193-9772-3-5

    Article  Google Scholar 

  18. Mell PM, Grance T (2011) The NIST definition of cloud computing. doi: 10.6028/NIST.SP.800-145

  19. Materials Resources LLC (2015) MiCloud. http://www.icmrl.com. Accessed 1 Jan 2017

  20. Materials Resources LLC (2016) MiCloud.AM for additive manufacturing. www.MiCloud.AM. Accessed 1 Jan 2017

  21. Seifi M, Salem A, Beuth J et al. (2016) Overview of materials qualification needs for metal additive manufacturing. JOM 68:747-764. doi: 10.1007/s11837-015-1810-0

    Article  Google Scholar 

  22. Salem AA, Shaffer JB, Satko DP et al (2014) Workflow for integrating mesoscale heterogeneities in materials structure with process simulation of titanium alloys. Integr Mater Manuf Innov 3:24. doi:10.1186/s40192-014-0024-6

    Article  CAS  Google Scholar 

  23. Salem AA, Kalidindi SR, Doherty RD, Semiatin SL (2006) Strain hardening due to deformation twinning in a-titanium: mechanisms. Acta Mater 37:259–268

    Google Scholar 

  24. Zhao P, Song En Low T, Wang Y, Niezgoda SR (2016) An integrated full-field model of concurrent plastic deformation and microstructure evolution: application to 3D simulation of dynamic recrystallization in polycrystalline copper. Int J Plast 80:38–55. doi:10.1016/j.ijplas.2015.12.010

    Article  CAS  Google Scholar 

  25. Lebensohn RA, Kanjarla AK, Eisenlohr P (2012) 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. doi:10.1016/j.ijplas.2011.12.005

    Article  Google Scholar 

  26. Satko DP, Shaffer JB, Tiley JS et al (2016) Effect of microstructure on oxygen rich layer evolution and its impact on fatigue life during high-temperature application of α/β titanium. Acta Mater 107:377–389. doi:10.1016/j.actamat.2016.01.058

    Article  CAS  Google Scholar 

  27. MSC software simufact—simulating manufacturing. http://www.simufact.de/en/index.html. Accessed 4 Jun 2015

  28. Rossant C (2016) Moving away from HDF5. http://cyrille.rossant.net/moving-away-hdf5/

  29. Schmitz GJ, Böttger B, Apel M et al (2016) Towards a metadata scheme for the description of materials—the description of microstructures. Sci Technol Adv Mater. doi:10.1080/14686996.2016.1194166

    Article  Google Scholar 

  30. The HDF group (2016) HDF5 HOME PAGE. https://support.hdfgroup.org/HDF5/

  31. MCNULTY E (2014) SQL VS. NOSQL- What you need to know. http://dataconomy.com/sql-vs-nosql-need-know/

  32. Germain L, Gey N, Humbert M et al (2005) Analysis of sharp microtexture heterogeneities in a bimodal IMI 834 billet. Acta Mater 53:3535–3543. doi:10.1016/j.actamat.2005.03.043

    Article  CAS  Google Scholar 

  33. Pilchak AL (2014) A simple model to account for the role of microtexture on fatigue and dwell fatigue lifetimes of titanium alloys. Scr Mater 74:68–71. doi:10.1016/j.scriptamat.2013.10.024

    Article  CAS  Google Scholar 

  34. Pilchak AL, Bhattacharjee A, Williams REA, Williams JC (2009) The effect of microstructure on fatigue crack initiation in Ti-6Al-4V. ICF12

  35. Woodfield AP, Gorman MD, Sutliff JA, Corderman RR (1995) Effect of microstructure on dwell fatigue behavior of Ti-6242. In: Titanium’95 Sci. Technol. Birmingham, UK, pp 1116–1123

  36. Venkatesh V, Tamirisa S, Sartkulvanich J et al. (2016) Icme of microtexture evolution in dual phase titanium alloys. In: Proc. 13th World Conf. Titan. Wiley, Inc., Hoboken, NJ, USA, pp 1907–1912

    Google Scholar 

  37. Qiu J, Ma Y, Lei J et al (2014) A comparative study on dwell fatigue of Ti-6Al-2Sn-4Zr-xMo (x = 2 to 6) alloys on a microstructure-normalized basis. Metall Mater Trans A 45:6075–6087. doi:10.1007/s11661-014-2541-5

    Article  CAS  Google Scholar 

  38. Pilchak AL, Szczepanski CJ, Shaffer JA et al (2013) Characterization of microstructure, texture, and microtexture in near-alpha titanium mill products. Metall Mater Trans A 44:4881–4890

    Article  CAS  Google Scholar 

  39. Semiatin SL, Seetharaman V, Weiss I (1996) Hot working of titanium alloys—an overview. Adv Sci Technol Titan Alloy Process 3–73

  40. Semiatin SL, Knisley SL, Fagin PN et al (2003) Microstructure evolution during alpha-beta heat treatment of Ti-6Al-4V. Metall Mater Trans A 34:2377–2386. doi:10.1007/s11661-003-0300-0

    Article  Google Scholar 

  41. Salem AA, Glavicic MG, Semiatin SL (2008) A coupled EBSD/EDS method to determine the primary- and secondary-alpha textures in titanium alloys with duplex microstructures. Mater Sci Eng A 494:350–359. doi:10.1016/j.msea.2008.06.022

    Article  CAS  Google Scholar 

  42. Kalidindi SR, Niezgoda SR, Salem AA (2011) Microstructure informatics using higher-order statistics and efficient data-mining protocols. JOM 63:34–41

    Article  CAS  Google Scholar 

  43. ACCESS (2009) Microstructure simulation using the software MICRESS

  44. ACCESS MICRESS. http://web.micress.de/

  45. EDAX Orientation Imaging Microscopy (OIM) Analysis. http://www.edax.com/Products/EBSD/OIM-Data-Analysis-Microstructure-Analysis.aspx. Accessed 1 Jan 2017

  46. Bruker Quantax EBSD. https://www.bruker.com/products/x-ray-diffraction-and-elemental-analysis/eds-wds-ebsd-sem-micro-xrf-and-sem-micro-ct/quantax-ebsd/overview.html. Accessed 1 Jan 2017

  47. Oxford AZtecHKL. https://www.oxford-instruments.com/products/microanalysis/ebsd/aztechkl-ebsd-software. Accessed 1 Jan 2017

  48. Yamrom B (1997) Method of color coding orientation information. 5

  49. Salem AA, Shaffer JB (2013) Identification and quantification of microtextured regions in materials with ordered crystal structure

  50. Bunge HJ (1982) Texture analysis in materials science: mathematical methods. Buttersworths, London

    Google Scholar 

Download references

Acknowledgements

Discussions with Dr. A. Pilchak, Dr. S.L. Semiatin, Dr. J. Calcaterra, and Dr. C. Ward of the Air Force Research Laboratory; Dr. T. Broderick and Dr. A. Woodfield of GE Aviation; Dr. M.G. Glavicic of Rolls Royce; Dr. V. Venkatesh of Pratt & Whitney; and Dr. S. Tamirisakandala of Arconic are gratefully acknowledged. Use and supply of software and datasets for MiCloud by Prof. S. Niezgoda (OSU), Prof. R. Srinivasan (WSU), Prof. J. Lewandowski (CWRU), and Dr. M. Seifi (ASTM International) are highly appreciated.

Authors’ Contributions

AAS conceived MiCloud concept and its interoperability and created the initial draft. JBS, RAK, LAW, and DPS helped with manuscript writing and analysis of exemplary datasets. All authors read and approved the final manuscript.

Compliance with Ethical Standards

ᅟ

Competing Interests

The authors are employees of Materials Resources. The TiZone algorithm is covered under US patent US9070203 B2.

Author information

Authors and Affiliations

  1. Materials Resources, LLC, Dayton, OH, USA

    Ayman A. Salem, Joshua B. Shaffer, Richard A. Kublik, Luke A. Wuertemberger & Daniel P. Satko

Authors
  1. Ayman A. Salem
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Joshua B. Shaffer
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Richard A. Kublik
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Luke A. Wuertemberger
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Daniel P. Satko
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ayman A. Salem.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Salem, A.A., Shaffer, J.B., Kublik, R.A. et al. Microstructure-Informed Cloud Computing for Interoperability of Materials Databases and Computational Models: Microtextured Regions in Ti Alloys. Integr Mater Manuf Innov 6, 111–126 (2017). https://doi.org/10.1007/s40192-017-0090-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40192-017-0090-7

Keywords

Search

Navigation