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Digital Image Correlation of Forescatter Detector Images for Simultaneous Strain and Orientation Mapping

Published online by Cambridge University Press:  06 July 2020

Derrik Adams ,
Shamoon Irfan ,
Jeff Cramer ,
Michael P. Miles ,
Eric R. Homer ,
Tyson Brown ,
Raj K. Mishra  and
David T. Fullwood
Derrik Adams
Affiliation:
Mechanical Engineering Department, Brigham Young University, Provo, UT, USA
Shamoon Irfan
Affiliation:
Mechanical Engineering Department, The NorthCap University, Gurugram, Haryana, India
Jeff Cramer
Affiliation:
Manufacturing Engineering Department, Brigham Young University, Provo, UT, USA
Michael P. Miles
Affiliation:
Manufacturing Engineering Department, Brigham Young University, Provo, UT, USA
Eric R. Homer
Affiliation:
Mechanical Engineering Department, Brigham Young University, Provo, UT, USA
Tyson Brown
Affiliation:
Research & Development Department, General Motors, Warren, MI, USA
Raj K. Mishra
Affiliation:
Research & Development Department, General Motors, Warren, MI, USA
David T. Fullwood*
Affiliation:
Mechanical Engineering Department, Brigham Young University, Provo, UT, USA
*
*Author for correspondence: David Fullwood, E-mail: dfullwood@byu.edu
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Abstract

Improved plasticity models require simultaneous experimental local strain and microstructural evolution data. Microscopy tools, such as electron backscatter diffraction (EBSD), that can monitor transformation at the relevant length-scale, are often incompatible with digital image correlation (DIC) techniques required to determine local deformation. In this paper, the viability of forescatter detector (FSD) images as the basis for the DIC study is investigated. Standard FSD and an integrated EBSD/FSD approach (Pattern Region of Interest Analysis System: PRIAS™) are analyzed. Simultaneous strain and microstructure maps are obtained for tensile deformation of Q&P 1180 steel up to ~14% strain. Tests on an undeformed sample that is simply shifted indicate a standard deviation of error in strain of around 0.4% without additional complications from a deformed surface. The method resolves strain bands at ~2 μm spacing but does not provide significant sub-grain strain resolution. Similar resolution was obtained for mechanically polished and electropolished samples, despite electropolished surfaces presenting a smoother, simpler topography. While the resolution of the PRIAS approach depends upon the EBSD step size, the 80 nm step size used provides seemingly similar resolution as 8,000× (22.7 nm) FSD images. Surface feature evolution prevents DIC analysis across large strain steps (>6% strain), but restarting DIC, using an FSD reference image from an interim strain step, allows reasonable DIC across the stress–strain curve. Furthermore, the data are obtained easily and provide complementary information for EBSD analysis.

Keywords

digital image correlation (DIC)electron backscatter detection (EBSD)forescatter detector (FSD)Pattern Region of Interest Analysis System (PRIAS)TRIP steel
Type
Software and Instrumentation
Information
Microscopy and Microanalysis , Volume 26 , Issue 4 , August 2020 , pp. 641 - 652
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Copyright
Copyright © Microscopy Society of America 2020

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References

Blaber, J (2018). NCORR v1.2. www.ncorr.com.Google Scholar
Blaber, J, Adair, B & Antoniou, A (2015). NCORR: Open-source 2D digital image correlation MATLAB software. Exp Mech 55(6), 1105–1122.CrossRefGoogle Scholar
Da Fonseca, JQ, Mummery, PM & Withers, PJ (2005). Full-field strain mapping by optical correlation of micrographs acquired during deformation. J Microsc 218, 9–21.CrossRefGoogle Scholar
Di Gioacchino, F & da Fonseca, JQ (2013). Plastic strain mapping with sub-micron resolution using digital image correlation. Exp Mech 53(5), 743–754.CrossRefGoogle Scholar
Fullwood, D, Adams, B, Basinger, J, Ruggles, T, Khosravani, A, Sorensen, C & Kacher, J (2014). Microstructure detail extraction via EBSD: An overview. In Strain and Dislocation Gradients from Diffraction: Spatially-Resolved Local Structure and Defects, Barabash, R & Ice, G (eds), pp. 405–437. London, UK: World Scientific.CrossRefGoogle Scholar
Fullwood, D, Niezgoda, S, Adams, B & Kalidindi, S (2010). Microstructure sensitive design for performance optimization. Prog Mater Sci 55, 477–562.CrossRefGoogle Scholar
Harte, A, Atkinson, M, Preuss, M & Fonseca, JQD. (In Review). A statistical study of the relationship between plastic strain and lattice misorientation on the surface of a deformed Ni-base superalloy. http://doi.org/10.5281/zenodo.3701817CrossRefGoogle Scholar
Kammers, AD & Daly, S (2011). Small-scale patterning methods for digital image correlation under scanning electron microscopy. Measure Sci Technol 22, 12.CrossRefGoogle Scholar
Kammers, AD & Daly, S (2013). Self-assembled nanoparticle surface patterning for improved digital image correlation in a scanning electron microscope. Exp Mech 53(8), 1333–1341.CrossRefGoogle Scholar
Kang, JD, Ososkov, Y, Embury, JD & Wilkinson, DS (2007). Digital image correlation studies for microscopic strain distribution and damage in dual phase steels. Script Mater 56(11), 999–1002.CrossRefGoogle Scholar
Lecompte, D, Smits, A, Bossuyt, S, Sol, H, Vantomme, J, Hemelrijck, DV & Habraken, AM (2006). Quality assessment of speckle patterns for digital image correlation. Optic Lasers Eng 44, 1132–1145.CrossRefGoogle Scholar
Nolze, G (2007). Image distortions in SEM and their influences on EBSD measurements. Ultramicroscopy 107(2–3), 172–183.CrossRefGoogle ScholarPubMed
Pan, B & Li, K (2011). A fast digital image correlation method for deformation measurement. Optic Lasers Eng 49(7), 841–847.CrossRefGoogle Scholar
Pan, B, Wu, DF & Xia, Y (2012). Incremental calculation for large deformation measurement using reliability-guided digital image correlation. Optic Lasers Eng 50(4), 586–592.CrossRefGoogle Scholar
Ruggles, T, Cluff, S, Miles, M, Fullwood, D, Daniels, C, Avila, A & Chen, M (2016). Ductility of advanced high-strength steel in the presence of a sheared edge. JOM 68(7), 1839–1849.CrossRefGoogle Scholar
Ruggles, TJ, Bomarito, GF, Cannon, AH & Hochhalter, JD (2017). Selectively electron-transparent microstamping toward concurrent digital image correlation and high-angular resolution electron backscatter diffraction (EBSD) analysis. Microsc Microanal 23(6), 1091–1095.CrossRefGoogle ScholarPubMed
Schreier, H, Orteu, J-J & Sutton, MA (2009). Image Correlation for Shape, Motion and Deformation Measurements. NY, USA: Springer.CrossRefGoogle Scholar
Sutton, MA, McNeill, SR, Helm, JD & Chao, YJ (2000). Advances in two-dimensional and three-dimensional computer vision. Photo-Mech 77, 323–372.Google Scholar
Sutton, M, Wolters, W, Peters, W, Ranson, W & McNeill, S (1983). Determination of displacements using an improved digital correlation method. Image Vision Comput 1(3), 133–139.CrossRefGoogle Scholar
Tasan, CC, Hoefnagels, JPM & Geers, MGD (2010). Microstructural banding effects clarified through micrographic digital image correlation. Script Mat 62(11), 835–838.CrossRefGoogle Scholar
Tatschl, A & Kolednik, O (2003). A new tool for the experimental characterization of micro-plasticity. Mat Sci Eng Struct Mat Proper Microstruct Process 339(1–2), 265–280.CrossRefGoogle Scholar
Tirumalasetty, GK (2013). Mechanics in steels through microscopy. MS (master of science) Thesis, Technical University of Munich, Germany.Google Scholar
Torquato, S (2002). Random Heterogeneous Materials. New York: Springer-Verlag.CrossRefGoogle Scholar
Wright, SI, Nowell, MM, de Kloe, R, Camus, P & Rampton, T (2015). Electron imaging with an EBSD detector. Ultramicroscopy 148, 132–145.CrossRefGoogle ScholarPubMed
Wright, SI, Nowell, MM & Field, DP (2011). A review of strain analysis using electron backscatter diffraction. Microsc Microanal 17(3), 316–329.CrossRefGoogle ScholarPubMed
Yan, DS, Tasan, CC & Raabe, D (2015). High resolution in situ mapping of microstrain and microstructure evolution reveals damage resistance criteria in dual phase steels. Acta Mater 96, 399–409.CrossRefGoogle Scholar
Zhang, TT, Jiang, J, Shollock, BA, Ben Britton, T & Dunne, FPE (2015). Slip localization and fatigue crack nucleation near a non-metallic inclusion in polycrystalline nickel-based superalloy. Mat Sci Eng Struct Mat Proper Microstruct Process 641, 328–339.CrossRefGoogle Scholar