The use of CAE in design decision-making has created a need for proven simulation accuracy. The two areas where simulation touches the ground are with material data and experimental verification and validation (V&V). Precise, well designed and quantitative experiments are key to ensure that the simulation initiates with correct material behavior. Similar validation experiments are needed to verify simulation and manage the risk associated with this predictive technology.
As part of Cornell University's mechanical engineering curriculum and study of classical beam theory, an aluminium beam is deformed to a specific load. Theoretical strains are calculated at certain points along the beam using beam theory, and then verified by using strain gauges placed at these points on the beam. This experiment is then extended to simulation of the same test setup in simulation software, where strains are analyzed at the same points. Discrepancies between the simulation, theory, and strain gauge results have often plagued the test, especially when incorporating more complex beam design. Through use of digital image correlation (DIC) it is possible to pinpoint some of the problem areas in the beam analysis and provide a better understanding of the localized strains that occur at any point in the deformed beam. The use of DIC provides a full field validation of simulation data, rather than a single spot check that strain gauges can provide. This validation technique helps to eliminate error that is associated with strain gauge placement and the possibility of missing strain hot spots that can arise when analyzing complex deformations or geometries.
There is interest in quantifying the differences between simulation and real life experimentation. This kind of work establishes a baseline for more complex simulations bringing a notion of traceability to the practice of CAE. We present the use of digital image correlation as a way to capture strain fields from component testing and compare these to simulation. Factors that are important in ensuring fidelity between simulation and experiment will be discussed.
The development of material parameters for FEA is heavily reliant on precision material data that captures the stress-strain relationship with fidelity. While conventional methods involving UTMs and extensometers are quite adequate for obtaining such data on a number of materials, there are important cases where they have been known to be inadequate. The testing of composites to obtain directional properties remains a complex task because of the difficulty related to measuring these properties in different orientations. Digital Image Correlation (DIC) methods are able to capture the stress-strain relationship all the way to failure. In this paper, we combine DIC and conventional methods to measure directional properties of composites. We exploit the unique capability of DIC to retroactively place virtual strain gauges in areas of critical interest in the test specimen. Utilising an Iosipescu fixture, we measure shear properties of structured composites in a variety of orientations to compute the parameters of an orthotropic linear elastic material model. Model consistency is checked by validation using Abaqus.
Today, CAE is integrated with modern automotive product development. This creates new challenges for departments that support new product development. In the materials arena, the testing is elevated to much higher levels of sophistication and precision to accommodate the complex material models used in CAE. It is no longer simple matter to convert raw data into material model parameters. We present an end-to-end strategy that gives automakers a well managed pathway to transforming to simulation-based design. We operate a quick-turnaround expert material testing lab to support high-end CAE and product development. We provide a data management software designed specifically to capture and display material data of any complexity. The software can transform raw material data into material parameter files for most commonly used simulations. The CAE Modeler software is of adequate sophistication to fit equations to data, visualize material models along with raw data, and output material cards. Examples for high strain-rate crash material modeling will be presented.
SAMP-1 is a complex material model designed to capture non-Mises yield and localization behavior in plastics. To perform well, it is highly dependent on accurate post-yield material data. A number of assumptions and approximations are currently used to translate measured stress-strain data into the material parameters related to these inputs. In this paper, we look at the use of direct localized strain measurements using digital image correlation (DIC) as a way to more directly extract the required data needed for SAMP-1.
LS-DYNA contains a wealth of material models that allow for the simulation of transient phenomena. These models are often quite complex and difficult to calibrate. We present CAE Modeler, a generalized pre-processor software used to convert material property data into material parameters for different material models used in CAE. In this paper, CAE Modeler is used to streamline the conversion of rate dependent stress-strain data into material parameters for the MAT_024 material model. The interactive software is capable of handling all three rate dependency options of MAT_024 and outputs a data file that can be read directly into LS-DYNA. Support for other material models is envisaged.
DatapointLabs' TestPaks (material testing + model calibration + Abaqus input decks) for rate-dependent, hyperelastic, viscoelastic, NVH, and the use of Abaqus CAE Modeler to transform raw data into material cards will be presented. A representative from Idiada will present a case study explaining the use of DatapointLabs’ material data and TestPaks for simulation.
The testing of materials for use in crash and safety simulations and the conversion of test data into material models is a process that is not well standardized in the industry. Consequently, CAE users face uncertainty and risk in this process that can have a negative impact on simulation quality. In this workshop, we present approaches currently used in the US for the gathering of high quality test data plus the acclaimed Matereality CAE Modeler software that is used to transform high strain-rate data into crash material cards.
We present a methodology for DIGIMAT users to perform the DIGIMAT MX reverse engineering process to obtain material parameter inputs for crash, elasto-plastic, creep and visco-elasticity. The injection-molding process used involves a standardized plaque geometry with fully developed flow, with test specimens taken from a specific plaque location. A standardized testing procedure is applied and the resulting DIGIMAT MX inputs are handled in a streamlined data stream, which saves time and improves the reliability of the reverse engineering process. The DIGIMAT MX reverse engineering itself can be performed as a service in collaboration with e-Xstream. This gives the user a speedy and tightly controlled process for performing complex finite element analysis with filled plastics
