196
J. Hochhalter et al.
surfaced-based approaches includes uncertainties to the calibration process due to
unknown subsurface features, such as grain variations and defects. For example, a
subsurface defect can influence the local strain on the surface as measured by DIC.
Using only a surface-based approach, this would manifest as additional (inaccurate)
variation in the calibrated parameter distribution. However, if that same subsurface
feature was detected using X-ray CT and included in the computational model, a
more accurate calibrated parameter distribution would be expected. Consequently,
an important next step is to quantify the improvement in calibration that can be
expected with volumetric acquisition methods and weigh those against the added
costs and time associated with acquiring that data.
References
1. S. Avril, M. Bonnet, A.S. Bretelle, M. Grédiac, F. Hild, P. Ienny, F. Latourte, D. Lemosse,
S. Pagano, E. Pagnacco, F. Pierron, Overview of identification methods of mechanical
parameters based on full-field measurements. Exp. Mech. 48(4), 381 (2008)
2. M. Bertin, C. Du, J.P. Hoefnagels, F. Hild, Crystal plasticity parameter identification with 3D
measurements and integrated digital image correlation. Acta Mater. 116, 321–331 (2016)
3. M. Bonnet, A. Constantinescu, Inverse problems in elasticity. Inverse Prob. 21(2), R1 (2005).
http://stacks.iop.org/0266-5611/21/i=2/a=R01
4. J.E. Bozek, J.D. Hochhalter, M.G. Veilleux, M. Liu, G. Heber, S.D. Sintay, A.D. Rollett, D.J.
Littlewood, A.M. Maniatty, H. Weiland, R.J. Christ, J. Payne, G. Welsh, D.G. Harlow, P.A.
Wawrzynek, A.R. Ingraffea, A geometric approach to modeling microstructurally small fatigue
crack formation: I. Probabilistic simulation of constituent particle cracking in AA 7075-T651.
Model. Simul. Mater. Sci. Eng. 16(6), 065007 (2008)
5. T. Britton, C. Maurice, R. Fortunier, J. Driver, A. Day, G. Meaden, D. Dingley, K. Mingard,
A. Wilkinson, Factors affecting the accuracy of high resolution electron backscatter diffraction
when using simulated patterns. Ultramicroscopy 110, 1443–1453 (2010)
6. S.P. Brooks, G.O. Roberts, Convergence assessment techniques for markov chain Monte Carlo.
Stat. Comput. 8(4), 319–335 (1998)
7. J. Brynjarsdóttir, A. O’Hagan, Learning about physical parameters: the importance of model
discrepancy. Inverse Prob. 30(11), 114007 (2014)
8. G.M. Castelluccio, D.L. Mcdowell, A mesoscale modeling of microstructurally small fatigue
cracks in metallic polycrystals. Mater. Sci. Eng. A 598, 34–55 (2014)
9. Z. Chen, W. Lenthe, J.C. Stinville, M. Echlin, T.M. Pollock, S. Daly, High-resolution
deformation mapping across large fields of view using scanning electron microscopy and
digital image correlation. Exp. Mech. 58(9), 1407–1421 (2018)
10. Y. Dong, B. Pan, A review of speckle pattern fabrication and assessment for digital image
correlation. Exp. Mech. 57(8), 1161–1181 (2017)
11. F. Gao, L. Han, Implementing the Nelder-Mead simplex algorithm with adaptive parameters.
Comput. Optim. Appl. 51(1), 259–277 (2012)
12. C. Geuzaine, J.F. Remacle, GMSH: a 3-D finite element mesh generator with built-in pre- and
post-processing facilities. Int. J. Numer. Methods Eng. 79(11), 1309–1331 (2009)
13. J. Geweke et al., Evaluating the accuracy of sampling-based approaches to the calculation
of posterior moments, vol. 196. Federal Reserve Bank of Minneapolis, Research Department
Minneapolis (1991)
14. M. Groeber, M. Jackson, DREAM.3D: a digital representation environment for the analysis of
microstructure in 3D. Integr. Mater. Manuf. Innov. 3(1), 5 (2014)
J. Hochhalter et al.
surfaced-based approaches includes uncertainties to the calibration process due to
unknown subsurface features, such as grain variations and defects. For example, a
subsurface defect can influence the local strain on the surface as measured by DIC.
Using only a surface-based approach, this would manifest as additional (inaccurate)
variation in the calibrated parameter distribution. However, if that same subsurface
feature was detected using X-ray CT and included in the computational model, a
more accurate calibrated parameter distribution would be expected. Consequently,
an important next step is to quantify the improvement in calibration that can be
expected with volumetric acquisition methods and weigh those against the added
costs and time associated with acquiring that data.
References
1. S. Avril, M. Bonnet, A.S. Bretelle, M. Grédiac, F. Hild, P. Ienny, F. Latourte, D. Lemosse,
S. Pagano, E. Pagnacco, F. Pierron, Overview of identification methods of mechanical
parameters based on full-field measurements. Exp. Mech. 48(4), 381 (2008)
2. M. Bertin, C. Du, J.P. Hoefnagels, F. Hild, Crystal plasticity parameter identification with 3D
measurements and integrated digital image correlation. Acta Mater. 116, 321–331 (2016)
3. M. Bonnet, A. Constantinescu, Inverse problems in elasticity. Inverse Prob. 21(2), R1 (2005).
http://stacks.iop.org/0266-5611/21/i=2/a=R01
4. J.E. Bozek, J.D. Hochhalter, M.G. Veilleux, M. Liu, G. Heber, S.D. Sintay, A.D. Rollett, D.J.
Littlewood, A.M. Maniatty, H. Weiland, R.J. Christ, J. Payne, G. Welsh, D.G. Harlow, P.A.
Wawrzynek, A.R. Ingraffea, A geometric approach to modeling microstructurally small fatigue
crack formation: I. Probabilistic simulation of constituent particle cracking in AA 7075-T651.
Model. Simul. Mater. Sci. Eng. 16(6), 065007 (2008)
5. T. Britton, C. Maurice, R. Fortunier, J. Driver, A. Day, G. Meaden, D. Dingley, K. Mingard,
A. Wilkinson, Factors affecting the accuracy of high resolution electron backscatter diffraction
when using simulated patterns. Ultramicroscopy 110, 1443–1453 (2010)
6. S.P. Brooks, G.O. Roberts, Convergence assessment techniques for markov chain Monte Carlo.
Stat. Comput. 8(4), 319–335 (1998)
7. J. Brynjarsdóttir, A. O’Hagan, Learning about physical parameters: the importance of model
discrepancy. Inverse Prob. 30(11), 114007 (2014)
8. G.M. Castelluccio, D.L. Mcdowell, A mesoscale modeling of microstructurally small fatigue
cracks in metallic polycrystals. Mater. Sci. Eng. A 598, 34–55 (2014)
9. Z. Chen, W. Lenthe, J.C. Stinville, M. Echlin, T.M. Pollock, S. Daly, High-resolution
deformation mapping across large fields of view using scanning electron microscopy and
digital image correlation. Exp. Mech. 58(9), 1407–1421 (2018)
10. Y. Dong, B. Pan, A review of speckle pattern fabrication and assessment for digital image
correlation. Exp. Mech. 57(8), 1161–1181 (2017)
11. F. Gao, L. Han, Implementing the Nelder-Mead simplex algorithm with adaptive parameters.
Comput. Optim. Appl. 51(1), 259–277 (2012)
12. C. Geuzaine, J.F. Remacle, GMSH: a 3-D finite element mesh generator with built-in pre- and
post-processing facilities. Int. J. Numer. Methods Eng. 79(11), 1309–1331 (2009)
13. J. Geweke et al., Evaluating the accuracy of sampling-based approaches to the calculation
of posterior moments, vol. 196. Federal Reserve Bank of Minneapolis, Research Department
Minneapolis (1991)
14. M. Groeber, M. Jackson, DREAM.3D: a digital representation environment for the analysis of
microstructure in 3D. Integr. Mater. Manuf. Innov. 3(1), 5 (2014)