VIVA: A Software Toolkit for Automated Multiscale In-Situ Effective Mechanical Property Prediction from Computed Tomography of As-Built Composites

ATA Engineering, Inc. (ATA), in collaboration with the National Institute for Aviation Research, proposes a project to advance VIVA (Volume Image-based Virtualization for Allowables), a software toolkit that provides physics-based prediction of multiscale in-situ effective mechanical properties of as-built laminated composites based on numerical reconstruction of X-ray computed tomography (XCT) data. The toolkit takes a multistage digital twin approach to automated property generation, starting with advanced rule-based and data-driven machine-learning (ML)-based image segmentation techniques to generate multiscale subsurface geometric models from input XCT data. These models include laminate structure features (such as ply waviness, orientation, interfaces, and drops) and critical defects and/or damage (such as ply wrinkles, voids, and resin-rich regions). These features and defects are reflected in multi-fidelity finite element (FE) models created by the software, forming a set of multiscale as-built representative volume element (RVE) models. Virtual characterization of the RVEs produces a coherent relationship between the material scales that is ultimately propagated through the component scale as a set of local mesoscale and global macroscale in-situ, nonlinear, anisotropic effective mechanical properties that are output as user-defined material databases encompassing various hygrothermal conditions enabling informed static/dynamic stress, fatigue, and progressive damage analyses. The proposed effort builds upon the Phase I project, which demonstrated feasibility of the approach and produced a software prototype capable of automating the generation of nonlinear, anisotropic mechanical properties for as-built composite structures, including defect effects. The Phase II project will extend the applicability and robustness of VIVA and assess its modeling and predictive accuracy through a verification and validation (V&V) test program. In particular, algorithms will be extended for fast automated XCT image processing, feature extraction, identification/classification, and uncertainty quantification (UQ) with uncertainty-aware ML techniques, as well as scalable data storage/retrieval/management. A key outcome will be generation of localized FE meshes from XCT scan data, capturing ply orientations and manufacturing defects. The integrated VIVA toolkit will be exercised to predict the in-situ effective mechanical properties for representative thick laminate composite structural components exhibiting defects. Experimental mechanical testing and characterization will be conducted to confirm the accuracy of VIVA predictions through comparison with physical data. Once software development and V&V tasks are completed, VIVA will provide an efficient, scalable, and accurate physics-based simulation capability that will enable more confident and cost-effective decision-making for repair limits and disposition of non-conforming composite components.