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A Data Analytics Approach to Discovering Unique Microstructural Configurations Susceptible to Fatigue

  • Data-driven Material Investigations: Understanding Fatigue Behavior
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Abstract

Principal component analysis and fuzzy c-means clustering algorithms were applied to slip-induced strain and geometric metric data in an attempt to discover unique microstructural configurations and their frequencies of occurrence in statistically representative instantiations of a titanium alloy microstructure. Grain-averaged fatigue indicator parameters were calculated for the same instantiation. The fatigue indicator parameters strongly correlated with the spatial location of the microstructural configurations in the principal components space. The fuzzy c-means clustering method identified clusters of data that varied in terms of their average fatigue indicator parameters. Furthermore, the number of points in each cluster was inversely correlated to the average fatigue indicator parameter. This analysis demonstrates that data-driven methods have significant potential for providing unbiased determination of unique microstructural configurations and their frequencies of occurrence in a given volume from the point of view of strain localization and fatigue crack initiation.

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References

  1. J.M. Larsen, S.K. Jha, C.J. Szczepanski, M.J. Caton, R. John, A.H. Rosenberger, D.J. Buchanan, P.J. Golden, and J.R. Jira, Int. J. Fatigue 57, 103 (2013).

    Article  Google Scholar 

  2. S.K. Jha, C.J. Szczepanski, R. John, and J.M. Larsen, Acta Mater. 82, 378 (2015).

    Article  Google Scholar 

  3. S.K. Jha, C.J. Szczepanski, P.J. Golden, W.J. Porter, and R. John, Int. J. Fatigue 42, 248 (2012).

    Article  Google Scholar 

  4. C.J. Szczepanski, S.K. Jha, J.M. Larsen, and J.W. Jones, Metall. Mater. Trans. A 39, 2841 (2008).

    Article  Google Scholar 

  5. V. Sinha, A.L. Pilchak, S.K. Jha, W.J. Porter, III, R. John, and J.M. Larsen, Metall. Mater. Trans. A 49, 1061 (2018). https://doi.org/10.1007/s11661-017-4437-7.

    Article  Google Scholar 

  6. F. Bridier, P. Villechaise, and J. Mendez, Acta Mater. 56, 3951 (2008).

    Article  Google Scholar 

  7. M.W. Libbrecht and W.S. Noble, Nat. Rev. Genet. 16, 321 (2015).

    Article  Google Scholar 

  8. D. Stavens and S. Thrun, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2010).

  9. M. Collins and N. Duffy, Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics (2004), p. 263–270.

  10. S.R. Niezgoda, A.K. Kanjarla, and S.R. Kalidindi, Integr. Mater. Manuf. Innov. 2, 3 (2013).

    Article  Google Scholar 

  11. B.L. DeCost and E.A. Holm, Comput. Mater. Sci. 110, 126 (2015).

    Article  Google Scholar 

  12. M.A. Groeber and M.A. Jackson, Integr. Mater. Manuf. Innov. 3, 5 (2014).

    Article  Google Scholar 

  13. I.T. Jolliffe, Principal Component Analysis, 2nd ed. (New York, NY: Springer, 2002), pp. 1–6.

    MATH  Google Scholar 

  14. A. Ben-Hur and I. Guyon, Functional Genomics: Methods and Protocols, ed. M.J. Brownstein and A. Kohodursky (Totowa: Humana Press, 2003), pp. 159–182.

    Chapter  Google Scholar 

  15. J.C. Bezdek, R. Ehrlich, and W. Full, Comput. Geosci. 10, 191 (1984).

    Article  Google Scholar 

  16. L. Méric, P. Poubanne, and G. Cailletaud, ASME J. Eng. Mater. Technol. 113, 162 (1991).

    Article  Google Scholar 

  17. Abaqus User Subroutines Reference Guide, Version 14.5, Dassault Systemès Simulia, Providence, RI, 2014.

  18. A. Fatemi and D.F. Socie, Fatigue Fract. Eng. Mater. Struct. 11, 149 (1988).

    Article  Google Scholar 

  19. A. Manonukul and F.P.E. Dunne, Proc. R. Soc. Lond. Ser. A 460, 1881 (2004).

    Article  Google Scholar 

  20. D.L. McDowell and F.P.E. Dunne, Int. J. Fatigue 32, 1521 (2010).

    Article  Google Scholar 

  21. J.D. Hochhalter, D.J. Littlewood, M.G. Veilleux, J.E. Bozek, A.M. Maniatty, A.D. Rollett, and A.R. Ingraffea, Model. Simul. Mater. Sci. Eng. 19, 1 (2011).

    Article  Google Scholar 

  22. C.P. Przybyla and D.L. McDowell, Int. J. Plast. 27, 1871 (2011).

    Article  Google Scholar 

  23. B. Scholkopf, A. Smola, and K.R. Muller, Artificial Neural Networks—ICANN’97, p. 583–588, 2005

  24. Q. Wang, Kernel Principal Component Analysis and its Application in Face Recognition and Active Shape Models. arXiv:1207.3538 [cs.CV], 2012.

  25. MATLAB R2017a, Mathworks, Inc., Natick, MA, 2017.

  26. J.A. Hartigan and M.A. Wong, Algorithm AS 136: A k-means clustering algorithm. J. R. Stat. Soc. Ser. C 28, 100 (1979).

    MATH  Google Scholar 

  27. C.A. Sugar and G.M. James, J. Am. Stat. Assoc. 98, 750 (2003).

    Article  Google Scholar 

  28. L. Kaufman and P.J. Rousseeuw, Finding Groups in Data. An Introduction to Cluster Analysis (New York, NY: Wiley-Interscience, 1990).

    MATH  Google Scholar 

  29. R. Tibshirani, G. Walther, and T. Hastie, J. R. Stat. Soc. B 63, 411 (2001).

    Article  Google Scholar 

  30. M. Yan and K. Ye, Biometrics 63, 1031 (2007).

    Article  MathSciNet  Google Scholar 

  31. G.W. Milligan and M.C. Cooper, Psychometrica 50, 159 (1985).

    Article  Google Scholar 

  32. R. Jang and N. Hickey, Data Set Clustering Using Fuzzy C-Means Clustering, Matlab Central File Exchange, MathWorks Inc., 2002.

  33. S.K. Jha, R.A. Brockman, R.M. Hoffman, V. Sinha, W.J. Porter, III, D.J. Buchanan, A.L. Pilchak, J.M. Larsen, and R. John, Air Force Research Laboratory, Wright-Patterson Air Force Base, OH, unpublished research, 2018.

  34. D.T. Fullwood, S.R. Niezgoda, B.L. Adams, and S.R. Kalidindi, Prog. Mater Sci. 55, 477 (2010).

    Article  Google Scholar 

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Acknowledgements

This work was performed at the Air Force Research Laboratory, Materials and Manufacturing Directorate, AFRL/RXCM, Wright-Patterson Air Force Base, OH. The financial support of the Air Force Office of Scientific Research with Dr. David Stargel and Mr. James Fillerup as Program Managers is gratefully acknowledged.

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Authors and Affiliations

  1. Air Force Research Laboratory, Materials and Manufacturing Directorate, RXCM, Wright-Patterson Air Force Base, OH, 45433, USA

    A. L. Pilchak, J. M. Larsen & R. John

  2. University of Dayton Research Institute, 300 College Park, Dayton, OH, 45469, USA

    S. K. Jha, R. A. Brockman, R. M. Hoffman, W. J. Porter III & D. J. Buchanan

  3. UES Inc., 4401 Dayton-Xenia Rd., Dayton, OH, 45432, USA

    V. Sinha

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  1. S. K. Jha
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  2. R. A. Brockman
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  8. J. M. Larsen
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  9. R. John
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Correspondence to S. K. Jha.

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Jha, S.K., Brockman, R.A., Hoffman, R.M. et al. A Data Analytics Approach to Discovering Unique Microstructural Configurations Susceptible to Fatigue. JOM 70, 1147–1153 (2018). https://doi.org/10.1007/s11837-018-2881-5

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  • DOI: https://doi.org/10.1007/s11837-018-2881-5

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