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Informing Mechanical Model Development Using Lower-Dimensional Descriptions of Lattice Distortion

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Abstract

This paper describes a method combining in situ X-ray diffraction data and dimensionality reduction (local linear embedding) to inform the development of state variable plasticity models. The method is applied to developing a state variable plasticity model for pure nickel deformed in uniaxial tension in the small strain regime. Prior to model development, connections between state variables representing evolution of mobile dislocations and the lower-dimensional representations of the data are established. Correlations between lower-dimensional representation of data and state variable evolution motivate the introduction of new evolution equation terms to increase alignment between experiment and model. These terms capture dislocation interactions leading to hardening transients prior to steady-state plastic flow. The discussion focuses on interpreting these new evolution terms and outstanding issues associated with linking lower-dimensional representations of data to state variable evolution modeled with ordinary differential equations.

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References

  1. Winther G, Margulies L, Schmidt S, Poulsen HF (2004) Lattice rotations of individual bulk grains part II: correlation with initial orientation and model comparison. Acta Mater 52:2863–2872

    Article  CAS  Google Scholar 

  2. Schuren JC, Wong SL, Dawson PR, Miller MP (2014) Integrating experiments and simulations to estimate uncertainty in lattice strain measurements. J Strain Anal Eng Des 49:33–50

    Article  Google Scholar 

  3. Obstalecki M, Wong SL, Dawson PR, Miller MP (2014) Quantitative analysis of crystal scale deformation heterogeneity during cyclic plasticity using high-energy X-ray diffraction and finite-element simulation. Acta Mater 75:259–272

    Article  CAS  Google Scholar 

  4. Pokharel R, Lind J, Kanjarla AK, Lebensohn RA, Li SF, Kenesei P, Suter RM, Rollett AD (2014) Polycrystal plasticity: comparison between grain-scale observations of deformation and simulations. Annu Rev Condens Matter Phys 5:317–346

    Article  CAS  Google Scholar 

  5. Chatterjee K, Venkataraman A, Garbaciak T, Rotella J, Sangid M, Beaudoin AJ, Kenesei P, Park J, Pilchak A (2016) Study of grain-level deformation and residual stresses in Ti-7Al under combined bending and tension using high energy diffraction microscopy. Int J Solids Struct 94:35–49

    Article  Google Scholar 

  6. Naragani D, Sangid MD, Shade PA, Schuren JC, Sharma H, Park J-S, Kenesei P, Bernier JV, Turner TJ, Parr I (2017) Investigation of fatigue crack initiation from a non-metallic inclusion via high energy X-ray diffraction microscopy. Acta Mater 137:71–84

    Article  CAS  Google Scholar 

  7. Juul NY, Oddershede J, Beaudoin A, Chatterjee K, Koker MKA, Dale D, Shade P, Winther G (2017) Measured resolved shear stresses and Bishop-Hill stress states in individual grains of austenitic stainless steel. Acta Mater 141:388–404

    Article  CAS  Google Scholar 

  8. Pokharel R, Lebensohn RA (2017) Instantiation of crystal plasticity simulations for micromechanical modelling with direct input from microstructural data collected at light sources. Scr Mater 132:73–77

    Article  CAS  Google Scholar 

  9. Tari V, Lebensohn RA, Pokharel R, Turner TJ, Shade PA, Bernier JV, Rollett AD (2018) Validation of micro-mechanical FFT-based simulations using high energy diffraction microscopy on Ti-7Al. Acta Mater 154:273–283

    Article  CAS  Google Scholar 

  10. Bhattacharyya JJ, Nair S, Pagan DC, Tari V, Rollett AD, Agnew SR (2020) In-situ high energy X-ray diffraction study of the elastic response of a metastable \(\beta \)-titanium alloy. Acta Mater 197:300–308

    Article  CAS  Google Scholar 

  11. Bandyopadhyay R, Gustafson SE, Kapoor K, Naragani D, Pagan DC, Sangid MD (2020) Comparative assessment of backstress models using high-energy x-ray diffraction microscopy experiments and crystal plasticity finite element simulations. Int J Plast 136:102887

    Article  Google Scholar 

  12. Pagan DC, Shade PA, Barton NR, Park J-S, Kenesei P, Menasche DB, Bernier JV (2017) Modeling slip system strength evolution in Ti-7Al informed by in-situ grain stress measurements. Acta Mater 128:406–417

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  14. Brough DB, Wheeler D, Warren JA, Kalidindi SR (2017) Microstructure-based knowledge systems for capturing process-structure evolution linkages. Curr Opin Solid State Mater Sci 21:129–140

    Article  CAS  Google Scholar 

  15. Mangal A, Holm EA (2018) A comparative study of feature selection methods for stress hotspot classification in materials. Integr Mater Manuf Innov 7:87–95

    Article  Google Scholar 

  16. Kantzos C, Lao J, Rollett A (2019) Design of an interpretable convolutional neural network for stress concentration prediction in rough surfaces. Mater Charact 158:109961

    Article  CAS  Google Scholar 

  17. Yabansu YC, Iskakov A, Kapustina A, Rajagopalan S, Kalidindi SR (2019) Application of Gaussian process regression models for capturing the evolution of microstructure statistics in aging of nickel-based superalloys. Acta Mater 178:45–58

    Article  CAS  Google Scholar 

  18. Yabansu YC, Rehn V, Hötzer J, Nestler B, Kalidindi SR (2019) Application of Gaussian process autoregressive models for capturing the time evolution of microstructure statistics from phase-field simulations for sintering of polycrystalline ceramics. Modell Simul Mater Sci Eng 27:084006

    Article  CAS  Google Scholar 

  19. Li X, Dyck OE, Oxley MP, Lupini AR, McInnes L, Healy J, Jesse S, Kalinin SV (2019) Manifold learning of four-dimensional scanning transmission electron microscopy. NPJ Comput Mater 5:1–8

    Article  Google Scholar 

  20. Pagan DC, Phan TQ, Weaver JS, Benson AR, Beaudoin AJ (2019) Unsupervised learning of dislocation motion. Acta Mater 181:510–518

    Article  CAS  Google Scholar 

  21. Roweis ST, Saul LK (2000) Nonlinear dimensionality reduction by locally linear embedding. Science 290:2323–2326

    Article  CAS  Google Scholar 

  22. Saul LK, Roweis ST (2003) Think globally, fit locally: unsupervised learning of low dimensional manifolds. J Mach Learn Res 4:119–155

    Google Scholar 

  23. Shade PA, Blank B, Schuren JC, Turner TJ, Kenesei P, Goetze K, Suter RM, Bernier JV, Li SF, Lind J, Lienert U, Almer J (2015) A rotational and axial motion system load frame insert for in situ high energy X-ray studies. Rev Sci Instrum 86:093902

    Article  Google Scholar 

  24. Juul NY, Winther G, Dale D, Koker MK, Shade P, Oddershede J (2016) Elastic interaction between twins during tensile deformation of austenitic stainless steel. Scr Mater 120:1–4

    Article  CAS  Google Scholar 

  25. Pagan DC, Bernier JV, Dale D, Ko JP, Turner TJ, Blank B, Shade PA (2018) Measuring Ti-7Al slip system strengths at elevated temperature using high-energy X-ray diffraction. Scr Mater 142:96–100

    Article  CAS  Google Scholar 

  26. Estrin Y, Kubin L (1986) Local strain hardening and nonuniformity of plastic deformation. Acta Metall 34:2455–2464

    Article  Google Scholar 

  27. Kocks U (1976) Laws for work-hardening and low-temperature creep. J Eng Mater Technol 98:76–85

    Article  CAS  Google Scholar 

  28. Nabarro FRN, Basinski ZS, Holt D (1964) The plasticity of pure single crystals. Adv Phys 13:193–323

    Article  CAS  Google Scholar 

  29. Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J, Passos A, Cournapeau D, Brucher M, Perrot M, Duchesnay E (2011) Scikit-learn: machine learning in Python. J Mach Learn Res 12:2825–2830

    Google Scholar 

  30. Wold S, Esbensen K, Geladi P (1987) Principal component analysis. Chemometr Intell Lab Syst 2:37–52

    Article  CAS  Google Scholar 

  31. Narutani T, Takamura J (1991) Grain-size strengthening in terms of dislocation density measured by resistivity. Acta Metall Mater 39:2037–2049

    Article  CAS  Google Scholar 

  32. Kocks U, Argon A, Ashby M (1976) Thermodynamics and kinetics of slip. Volume 19 of Progress in materials science. Pergamon Press, Oxford

    Google Scholar 

  33. Kocks UF, Argon AS, Ashby MF (1975) Thermodynamics and kinetics of slip: Volume 19 of Progress in materials science. Pergamon Press, Oxford

    Google Scholar 

  34. Washburn J (1965) Intersection cross slip. Appl Phys Lett 7:183–185

    Article  Google Scholar 

  35. Rao SI, Dimiduk DM, El-Awady JA, Parthasarathy TA, Uchic MD, Woodward C (2009) Atomistic simulations of cross-slip nucleation at screw dislocation intersections in face centered cubic nickel. Philos Mag 89:3351–3369

    Article  CAS  Google Scholar 

  36. Rao SI, Dimiduk DM, El-Awady JA, Parthasarathy TA, Uchic MD, Woodward C (2010) Activated states for cross-slip at screw dislocation intersections in face-centered cubic nickel and copper via atomistic simulation. Acta Mater 58:5547–5557

    Article  CAS  Google Scholar 

  37. Argon AS, Brydges W (1968) Deformation of copper in easy glide. Philos Mag 18:817–837

    Article  CAS  Google Scholar 

  38. Donoho DL, Grimes C (2003) Hessian eigenmaps: locally linear embedding techniques for high-dimensional data. Proc Nat Acad Sci 100:5591–5596

    Article  CAS  Google Scholar 

  39. Holiday A, Kooshkbaghi M, Bello-Rivas JM, Gear CW, Zagaris A, Kevrekidis IG (2019) Manifold learning for parameter reduction. J Comput Phys 392:419–431

    Article  Google Scholar 

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Acknowledgements

This work is based upon research conducted at the Center for High-Energy X-ray Sciences (CHEXS) which is supported by the National Science Foundation under award DMR-1829070. GHS and AJB received support through the Office of Naval Research (Contract N00014-16-1-3126). We would like to thank Dr. Edward Trigg for helpful discussions regarding orientation of the lower-dimensional embeddings. We would also like to thank Professor Matthew Miller for helpful discussions regarding this manuscript.

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Correspondence to Darren C. Pagan.

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Pagan, D.C., Schmidt, G.H., Borum, A.D. et al. Informing Mechanical Model Development Using Lower-Dimensional Descriptions of Lattice Distortion. Integr Mater Manuf Innov 9, 459–471 (2020). https://doi.org/10.1007/s40192-020-00196-y

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