Publications

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Modeling material and component behavior using finite element analysis (FEA) is critical for modern engineering. One key to a credible model is having an accurate material model, with calibrated model parameters, which describes the constitutive relationship between the deformation and the resulting stress in the material. As such, identifying material model parameters is critical to accurate and predictive FEA. Traditional calibration approaches use only global data (e.g. extensometers and resultant force) and simplified geometries to find the parameters. However, the utilization of rapidly maturing full-field characterization techniques (e.g. Digital Image Correlation (DIC)) with inverse techniques (e.g. the Virtual Feilds Method (VFM)) provide a new, novel and improved method for parameter identification. This LDRD tested that idea: in particular, whether more parameters could be identified per test when using full-field data. The research described in this report successfully proves this hypothesis by comparing the VFM results with traditional calibration methods. Important products of the research include: verified VFM codes for identifying model parameters, a new look at parameter covariance in material model parameter estimation, new validation techniques to better utilize full-field measurements, and an exploration of optimized specimen design for improved data richness.

This project targeted a full-field understanding of the conversion of plastic work into heat using advanced diagnostics (digital image correlation, DIC, combined with infrared, IR, imaging). This understanding will act as a catalyst for reformulating the prevalent simplistic model, which will ultimately transform Sandia's ability to design for and predict thermomechanical behavior, impacting national security applications including nuclear weapon assessments of accident scenarios. Tensile 304L stainless steel dogbones are pulled in tension at quasi-static rates until failure and full-field deformation and temperature data are captured, while accounting for thermal losses. The IR temperature fields are mapped onto the DIC coordinate system (Lagrangian formulation). The resultant fields are used to calculate the Taylor-Quinney coefficient, β, at two strain rates rates (0.002 s^-1 and 0.08 s^-1) and two temperatures (room temperature, RT, and 250°C).

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Traditionally, material identification is performed using global load and displacement data from simple boundary-value problems such as uni-axial tensile and simple shear tests. More recently, however, inverse techniques such as the Virtual Fields Method (VFM) that capitalize on heterogeneous, full-field deformation data have gained popularity. In this work, we have written a VFM code in a finite-deformation framework for calibration of a viscoplastic (i.e. strain-rate dependent) material model for 304L stainless steel. Using simulated experimental data generated via finite-element analysis (FEA), we verified our VFM code and compared the identified parameters with the reference parameters input into the FEA. The identified material model parameters had surprisingly large error compared to the reference parameters, which was traced to parameter covariance and the existence of many essentially equivalent parameter sets. This parameter non-uniqueness and its implications for FEA predictions is discussed in detail. Lastly, we present two strategies to reduce parameter covariance – reduced parametrization of the material model and increased richness of the calibration data – which allow for the recovery of a unique solution.

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Partial penetration laser welds join metal surfaces without additional filler material, providing hermetic seals for a variety of components. The crack-like geometry of a partial penetration weld is a local stress riser that may lead to failure of the component in the weld. Computational modeling of laser welds has shown that the model should include damage evolution to predict the large deformation and failure. We have performed interrupted tensile experiments both to characterize the damage evolution and failure in laser welds and to aid computational modeling of these welds. Several EDM-notched and laser-welded 304L stainless steel tensile coupons were pulled in tension, each one to a different load level, and then sectioned and imaged to show the evolution of damage in the laser weld and in the EDM-notched parent 304L material (having a similar geometry to the partial penetration laser-welded material). SEM imaging of these specimens revealed considerable cracking at the root of the laser welds and some visible micro-cracking in the root of the EDM notch even before peak load was achieved in these specimens. The images also showed deformation-induced damage in the root of the notch and laser weld prior to the appearance of the main crack, though the laser-welded specimens tended to have more extensive damage than the notched material. These experiments show that the local geometry alone is not the cause of the damage, but also microstructure of the laser weld, which requires additional investigation.

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This memo addresses the calibration of anisotropic yield functions based on data obtained from a series of uniaxial tension specimens extracted from a tubular Al 7079 circular cylindrical extrusion. Achieving the calibrations completed an important step in the Experimentally Enhanced Computations (ExEC) project. The focus of the project is on novel calibration approaches that will be based on advanced diagnostics and numerical simulations with the intention of reducing the overall calibration effort. The test data used here resulted from traditional tensile tests on specimens cut at 12 orientations within the extrusion. Two anisotropic yield surfaces — Hill’s (1948) and Barlat’s (2005) — were calibrated based on the test data. The methods used to conduct the calibrations are described, and the results show that the material exhibited significant yield anisotropy. The larger number of parameters in Barlat’s yield function allowed it to fit the test data more accurately than Hill’s. Although work remains to assess the sensitivity of the calibrated model parameters to various factors, the methods implemented and the results obtained here provide bases for further work and useful benchmarks for future calibrations to be conducted using the novel approach.

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A “good” speckle pattern enables DIC to make its full-field measurements, but oftentimes this artistic part of the DIC setup takes a considerable amount of time to develop and evaluate for a given optical configuration. A catalog of well-quantified speckle patterns for various fields of view would greatly decrease the time it would take to start making DIC measurements. The purpose of this speckle patterning study is to evaluate various speckling techniques we had readily available in our laboratories for fields of view from around 100 mm down to 5 mm that are common for laboratory-scale experiments. The list of speckling techniques is not exhaustive: spray painting, UV-printing of computer-designed speckle patterns, airbrushing, and particle dispersion. First, we quantified the resolution of our optical configurations for each of the fields of view to determine the smallest speckle we could resolve. Second, we imaged several speckle patterns at each field of view. Third, we quantified the average and standard deviation of the speckle size, speckle contrast, and density to characterize the quality of the speckle pattern. Finally, we performed computer-aided sub-pixel translation of the speckle patterns and ran correlations to examine how well DIC tracked the pattern translations. We discuss our metrics for a “good” speckle pattern and outline how others may perform similar studies for their desired optical configurations.

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