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DIC Data Driven Methods to Verify Simplifying Assumptions and Increase Confidence in Material Properties of Laminated Composites

GUILLAUME SEON, ANDREW MAKEEV, BASTIAAN VAN DER VOSSEN, JOSEPH SCHAEFER, BRIAN JUSTUSSON

Abstract


Over the last six years, the University of Texas Arlington Advanced Materials and Structures Lab (AMSL) has been developing a number of Digital Image Correlationbased and computed tomography-based advanced measurement techniques for material characterization as well as effective computational analysis methods to verify simplifying assumptions and increase confidence in material allowables for laminated composites [1]. DIC data-driven methods developed at AMSL include (1) Short-Beam Shear (SBS) Method, and (2) Small- Plate Twist (SPT) Method – simultaneously measuring nonlinear shear material properties in the 1-2, 1-3, and 2-3 material planes. The methodology to generate nonlinear stress-strain response has been objective enough so one can start from measuring the strain and calculating the stress independent of the strain measurement and use iterative FEM-based stress Updating (FEMU) after the initial geometric stress approximation to generate the material stress-strain relations free of ad hoc assumptions. The DIC data-driven modeling methodology developed at AMSL has been verified by Boeing under the High-Fidelity Experimental and Analytical Characterization of Input Properties for Progressive Damage Analysis Methods. In particular, lamina shear stress-strain curves have been generated in this work using SPT and SBS methods for a material that has no basis for a transverse isotropic approximation. SBS/SPT testing relying on DIC resulted in excellent-quality nonlinear stress-strain curves for 1-2 in-plane shear, and 1-3 as well as 2-3 interlaminar shear (ILS) behavior. It is worth noting that to this date SPT has been the only method to fully measure 2-3 ILS stress-strain curves including the nonlinear near-strength regime. This work, a collaborative effort between AMSL and the Boeing Company, presents some of the most recent results and ongoing activities related to DIC datadriven modeling.


DOI
10.12783/asc2017/15267

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