Development of Optimal Multiscale Patterns for Digital Image Correlation via Local Grayscale Variation

In some applications of digital image correlation (DIC), adequately quantifying deformation of a material can require identification of local deformations which are much smaller than the total field of interest. Instead of exhaustively stitching together images taken at high magnification, it is more efficient to utilize multiple magnifications. Unfortunately, it is rare that the material naturally has features that are useful for image correlation at multiple magnifications. Therefore, an ideal pattern was sought that (1) contains features appropriate for the multiple magnifications, (2) need not know location of high magnification a priori, and (3) can be viewed with standard DIC equipment. An optimization framework was developed based on the inclusion of local grayscale biases which can produce multiscale DIC patterns that satisfy these criteria. Numerical and physical experiments were also performed to illustrate the functionality and utility of the designed patterns

Selectively Electron-Transparent Microstamping Toward Concurrent Digital Image Correlation and High-Angular Resolution Electron Backscatter Diffraction (EBSD) Analysis

High resolution digital image correlation (HRDIC) and high resolution electron backscatter diffraction (HREBSD) provide valuable and complementary data concerning local deformation at the microscale. However, standard surface preparation techniques are mutually exclusive, which makes combining these techniques in situ impossible. This paper introduces a new method of applying surface patterning for HRDIC, namely a urethane rubber microstamp, that provides a pattern with enough contrast for HRDIC at low accelerating voltages, but is still virtually transparent at the higher voltages necessary for HREBSD and conventional electron backscatter diffraction (EBSD) analysis. Furthermore, microstamping is inexpensive and repeatable, and is more amenable to application of patterns to complex surface geometries and larger surface areas than other patterning techniques

New Levels of High Angular Resolution EBSD Performance via Inverse Compositional Gauss-Newton Digital Image Correlation

Conventional high angular resolution electron backscatter diffraction (HREBSD) uses cross-correlation to track features between diffraction patterns, which are then related to the relative elastic strain and misorientation between the diffracting volumes of material. This paper adapts inverse compositional Gauss Newton (ICGN) digital image correlation (DIC) to be compatible with HREBSD. ICGN works by efficiently tracking not just the shift in features, but also the change in their shape. Modeling a shape change as well as a shift results in greater accuracy. This method, ICGN HREBSD, is applied to a simulated data set, and its performance is compared to conventional cross-correlation HREBSD, and cross-correlation HREBSD with remapping. ICGN HREBSD is shown to have about half the strain error of the best cross-correlation method with a comparable computation time

The second Sandia Fracture Challenge: predictions of ductile failure under quasi-static and moderate-rate dynamic loading

© 2016, The Author(s). Ductile failure of structural metals is relevant to a wide range of engineering scenarios. Computational methods are employed to anticipate the critical conditions of failure, yet they sometimes provide inaccurate and misleading predictions. Challenge scenarios, such as the one presented in the current work, provide an opportunity to assess the blind, quantitative predictive ability of simulation methods against a previously unseen failure problem. Rather than evaluate the predictions of a single simulation approach, the Sandia Fracture Challenge relies on numerous volunteer teams with expertise in computational mechanics to apply a broad range of computational methods, numerical algorithms, and constitutive models to the challenge. This exercise is intended to evaluate the state of health of technologies available for failure prediction. In the first Sandia Fracture Challenge, a wide range of issues were raised in ductile failure modeling, including a lack of consistency in failure models, the importance of shear calibration data, and difficulties in quantifying the uncertainty of prediction [see Boyce et al. (Int J Fract 186:5–68, 2014) for details of these observations]. This second Sandia Fracture Challenge investigated the ductile rupture of a Ti–6Al–4V sheet under both quasi-static and modest-rate dynamic loading (failure in (Formula presented.) 0.1 s). Like the previous challenge, the sheet had an unusual arrangement of notches and holes that added geometric complexity and fostered a competition between tensile- and shear-dominated failure modes. The teams were asked to predict the fracture path and quantitative far-field failure metrics such as the peak force and displacement to cause crack initiation. Fourteen teams contributed blind predictions, and the experimental outcomes were quantified in three independent test labs. Additional shortcomings were revealed in this second challenge such as inconsistency in the application of appropriate boundary conditions, need for a thermomechanical treatment of the heat generation in the dynamic loading condition, and further difficulties in model calibration based on limited real-world engineering data. As with the prior challenge, this work not only documents the ‘state-of-the-art’ in computational failure prediction of ductile tearing scenarios, but also provides a detailed dataset for non-blind assessment of alternative methods

The second Sandia Fracture Challenge: predictions of ductile failure under quasi-static and moderate-rate dynamic loading

Ductile failure of structural metals is relevant to a wide range of engineering scenarios. Computational methods are employed to anticipate the critical conditions of failure, yet they sometimes provide inaccurate and misleading predictions. Challenge scenarios, such as the one presented in the current work, provide an opportunity to assess the blind, quantitative predictive ability of simulation methods against a previously unseen failure problem. Rather than evaluate the predictions of a single simulation approach, the Sandia Fracture Challenge relies on numerous volunteer teams with expertise in computational mechanics to apply a broad range of computational methods, numerical algorithms, and constitutive models to the challenge. This exercise is intended to evaluate the state of health of technologies available for failure prediction. In the first Sandia Fracture Challenge, a wide range of issues were raised in ductile failure modeling, including a lack of consistency in failure models, the importance of shear calibration data, and difficulties in quantifying the uncertainty of prediction [see Boyce et al. (Int J Fract 186:5–68, 2014) for details of these observations]. This second Sandia Fracture Challenge investigated the ductile rupture of a Ti–6Al–4V sheet under both quasi-static and modest-rate dynamic loading (failure in ∼∼ 0.1 s). Like the previous challenge, the sheet had an unusual arrangement of notches and holes that added geometric complexity and fostered a competition between tensile- and shear-dominated failure modes. The teams were asked to predict the fracture path and quantitative far-field failure metrics such as the peak force and displacement to cause crack initiation. Fourteen teams contributed blind predictions, and the experimental outcomes were quantified in three independent test labs. Additional shortcomings were revealed in this second challenge such as inconsistency in the application of appropriate boundary conditions, need for a thermomechanical treatment of the heat generation in the dynamic loading condition, and further difficulties in model calibration based on limited real-world engineering data. As with the prior challenge, this work not only documents the ‘state-of-the-art’ in computational failure prediction of ductile tearing scenarios, but also provides a detailed dataset for non-blind assessment of alternative methods.National Science Foundation (U.S.