SBIR/STTR Award attributes
Recent advancements in industrial Computed Tomography (CT), including improved X-ray detectors and tomographic reconstruction algorithms, have enabled enhanced identification of material anomalies and manufacturing defects. For composite structures, this nondestructive inspection methodology can enable dimensionally-accurate internal structure characterization of a component, facilitating the inspection process of defect distributions in as-built components before they enter service. Despite the advancements, there is an immediate need to develop digital models that are representatively identical to physical parts to enable accurate predictions of the load bearing capabilities of the components. A streamlined workflow is necessary to digitally thread the image segmentation process of the CT scans with the component characteristics (down to the ply level) that are computer simulation-ready. Ideally, the workflow should automatically assign fiber orientations (for fiber reinforced composites), create a volumetric mesh, and define appropriate inputs for different FEA packages, producing a modeling process that is robust and repeatable. Such high-accuracy computational models are expected to capture component-specific defects (e.g., ply interface voids, wrinkles) and further provide a more accurate prediction of stress concentrations and quantitative damage tolerance assessments. This work will be conducted in partnership with Drexel University to leverage its research expertise in composite damage mechanics.
