Application of Digital Image Correlation and Acoustic Emission to Determining Materials Properties of Carbon Fiber Composites
Hisham Sawan (The Ohio State University), Mark Walter (Ohio State University)
Eringen Medal Symposium in honor of G. Ravichandran
Mon 10:45 - 12:15
Salomon 001
In recent years most automotive companies have become much more serious about using carbon fiber composites for light-weighting automotive structures. In order more quickly and accurately design structures that perform at or above current safety standards, engineers look towards numerical simulation using experimentally determined material constants. However, complete characterization of composites requires a large number of expensive and time-consuming experiments. The advancement of full-field, digital image correlation, measurement techniques allows better performance of inverse methods for determination of elastic constants. This paper deals with identifying the constitutive parameters affecting the in-plane deformation response of an anisotropic material subject to a state of plane stress. Two different methods are formulated. The first method depends on satisfying the governing differential equations, utilizes some information about the boundary conditions, and does not require solving the forward problem. The second method is a Finite Element Model Update (FEMU) approach that focuses on simplifying the forward problem and reducing the calculation time per iteration. Both methods are applied to experimental data from testing unidirectional carbon fiber reinforced epoxy. Results from proposed approaches are compared to results obtained from standard testing. In the second part of this work, composites with holes are evaluated with digital image correlation as well as acoustic emission. Unsupervised learning techniques are applied to the AE data. The resulting AE groups are then related to the different failure mechanisms based on experimental observations and simulation-predicted behavior. AE parameters, which characterize the different groups, are identified for potential use in real time damage monitoring.