The purpose of this document is to provide evidence for assessing the adequacy of parameterized material models for a collection of materials used in a finite element analyses setting. “Adequacy” is relative to the intended use of the material in particular analyses. The intended application of the material models covered within this document is for system level abnormal mechanical solid mechanics analyses. Generally, material model parameterizations should be valid from temperatures of approximately -50 to 70° C, across a range of strain rates, and (depending on details of the parts involved) large deformations. Each material covered in this document is presented in its own chapter with a common format across materials. Model assumptions, limitations, existing validation results, readiness for use with uncertainty quantification, general usage guidance, and failure considerations are all provided along with specific parameterization inputs suitable for the finite element analysis code Sierra/Solid Mechanics.
In this paper we introduce a method to compare sets of full-field data using Alpert tree-wavelet transforms. The Alpert tree-wavelet methods transform the data into a spectral space allowing the comparison of all points in the fields by comparing spectral amplitudes. The methods are insensitive to translation, scale and discretization and can be applied to arbitrary geometries. This makes them especially well suited for comparison of field data sets coming from two different sources such as when comparing simulation field data to experimental field data. We have developed both global and local error metrics to quantify the error between two fields. We verify the methods on two-dimensional and three-dimensional discretizations of analytical functions. We then deploy the methods to compare full-field strain data from a simulation of elastomeric syntactic foam.
The Virtual Fields Method (VFM) is an inverse technique used for parameter estimation and calibration of constitutive models. Many assumptions and approximations—such as plane stress, incompressible plasticity, and spatial and temporal derivative calculations—are required to use VFM with full-field deformation data, for example, from Digital Image Correlation (DIC). This work presents a comprehensive discussion of the effects of these assumptions and approximations on parameters identified by VFM for a viscoplastic material model for 304L stainless steel. We generated synthetic data from a Finite-Element Analysis (FEA) in order to have a reference solution with a known material model and known model parameters, and we investigated four cases in which successively more assumptions and approximations were included in the data. We found that VFM is tolerant to small deviations from the plane stress condition in a small region of the sample, and that the incompressible plasticity assumption can be used to estimate thickness changes with little error. A local polynomial fit to the displacement data was successfully employed to compute the spatial displacement gradients. The choice of temporal derivative approximation (i.e., backwards difference versus central difference) was found to have a significant influence on the computed rate of deformation and on the VFM results for the rate-dependent model used in this work. Finally, the noise introduced into the displacement data from a stereo-DIC simulator was found to have negligible influence on the VFM results. Evaluating the effects of assumptions and approximations using synthetic data is a critical first step for verifying and validating VFM for specific applications. The results of this work provide the foundation for confidently using VFM for experimental data.
The third Sandia Fracture Challenge highlighted the geometric and material uncertainties introduced by modern additive manufacturing techniques. Tasked with the challenge of predicting failure of a complex additively-manufactured geometry made of 316L stainless steel, we combined a rigorous material calibration scheme with a number of statistical assessments of problem uncertainties. Specifically, we used optimization techniques to calibrate a rate-dependent and anisotropic Hill plasticity model to represent material deformation coupled with a damage model driven by void growth and nucleation. Through targeted simulation studies we assessed the influence of internal voids and surface flaws on the specimens of interest in the challenge which guided our material modeling choices. Employing the Kolmogorov–Smirnov test statistic, we developed a representative suite of simulations to account for the geometric variability of test specimens and the variability introduced by material parameter uncertainty. This approach allowed the team to successfully predict the failure mode of the experimental test population as well as the global response with a high degree of accuracy.
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. Finally, 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.
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 tech- niques (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 tech- niques to better utilize full-field measurements, and an exploration of optimized specimen design for improved data richness.