Material Testing and Data Management to be Showcased at Technical Meetings Globally.
DatapointLabs Expands Composite Testing to Meet Industry Demand.
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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.
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.
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.
Ultra-high molecular weight polyethylene (UHMWPE) is used extensively in orthopedic applications within the human body. Components made from these materials are subject to complex loading over extended periods of time. Modeling of components used in such applications depends heavily on having material data under in-vivo conditions. We present mechanical and visco-elastic properties measured in saline at 37C. Comparisons to conventionally measured properties at room temperature are made.
The limitations of modeling materials for simulation are discussed, including lack of clarity in material model requirements, gaps between the material data and the model to which it will be fitted, issues in obtaining pertinent properties, difficulties in parameter conversion (fitting), and preparation of input files for the software being used. Means to address these limitations are presented, including understanding the model completely, measuring the correct data with precision on the right material, selecting the best model for the data and ensuring the best fit of the model to the data, validating the model against a simple experiment, and following best practices to create an error-free input file.
Many material models are available for crash simulation. However, common models are not designed for plastics. We present best practices developed for adapting common models to plastics, as well as best testing protocols to generate clean, accurate rate-dependent data. In addition, we present a streamlined process to convert raw data to LS-DYNA material cards, and harmonized material datasets that allow the same raw data to be used for other crash and rate-dependent analysis software.
High strain rate material modelling of polymers for use in crash and drop testing has been plagued by a number of problems. These include poor quality and noisy data, material models unsuited to polymer behaviour and unclear material model calibration guidelines. The modelling of polymers is thus a risky proposition with a highly variable success rate. In previous work, we tackled each of the above problems individually. In this paper, we summarize and then proceed to present a material modelling strategy that can be applied for a wide variety of polymers.