This opportunity targets the development of a software toolkit to automate the generation of nonlinear, anisotropic mechanical properties for as-built composite structures, including the effects of defects, to accelerate finite element (FE) analysis for fleet repairs and aircraft production non-conformal dispositions. To meet this objective, Navy wants to implement a data-driven, physics-based, modeling approach including material structure and full-field deformation by analyzing the parts in the as-built condition with their own unique configuration, including manufacturing defects and service damage. Accordingly, the advances in the state of the art of the in-situ Computed Tomography (CT), Scanning Electron Microscopy (SEM), and Digital Image Correlation (DIC) / Direct Strain Imaging (DSI) data driven multiscale micro-meso-macro mechanics modeling are proposed to meet the material input data requirements for predicting residual strength and fatigue life of the as-built composite structures. The data/measurement resolution requirements include the appropriate length scale(s) associated with material system components (e.g., ply thickness/orientation, fiber path/bundle/volume, fiber/resin, and adhesive interfaces) and manufacturing defects (e.g., porosities/voids, wrinkles, delamination, and fiber waviness). The most critical defects, affecting structural integrity, include combinations of wrinkles, porosity/voids, and resin-rich or adhesive-rich zones, which will be captured by the model with an effective relationship to the FE mesh and intended analysis. The proposed toolkit would also account for material degradation due to repeated loadings and Hot/Wet (H/W) operating environments. Due to the size of the data for a full-scale component, speed and accuracy issues relating to data acquisition, image processing, and data storage and retrieval will also be addressed, including the use of machine learning (ML) techniques. The Phase I effort will demonstrate technical feasibility of the proposed concept to develop a computationally efficient, multiscale, physics-based, modeling toolkit coupled with CT-scanned data, machine learning, and computer vision techniques to generate in-situ, quasi-static, and dynamic effective mechanical properties (stiffness, strength, and strain energy release rate) for as-built, thick laminate composite structures, including effects of defects, repeated loadings, and expected H/W operating environments. Demonstrate the proposed workflow to auto-populate the input data for different 2-D and 3-D FE meshes, including various element sizes and types to support progressive damage analysis of thick laminate composite structures. Develop a verification and validation (V & V) test plan for the proposed concept, including, at a minimum, the use of DIC.
Benefit: The tools automating the generation of the in-situ material properties, capturing the manufacturing irregularities and their effects on structural integrity of composite parts, implemented into commercial-off-the-shelf finite element analysis software, will accelerate certification modeling of composites for rotary-wing and fixed-wing aircraft structures. The tools developed in this effort will enable effective integration of design, manufacturing, and certification of composite parts. The ability to design and build critical composite parts right the first time has been desperately needed by industry. For example, currently about 40% of the rotorcraft OEMs budget to design a new part is already spent before starting fatigue qualification. As a result, the part cannot be redesigned after problems are found; and the design can only be patched. The analysis tools enabling accurate structural strength and fatigue predictions for composite parts, capturing the effects of defects will assist aircraft OEMs and material manufacturers, closing the loop on effective control of the manufacturing process to minimize formation of the critical defects in composite parts and avoid costly trial and error empirical iterations. Based on the feedback provided to Team Members, the Rotorcraft Industry and research labs are anxious to acquire the tools developed in the proposed effort for immediate applications in the aircraft designs including USN V-22, H-1, CH-53K, Triton, US Army JMR and FVL aircraft and USAF Platforms. Similar feedback has been received from the fixed-wing aircraft OEMs. Total operational cost reduction involves using fleet-representative models to streamline reliability centered maintenance, improve aircraft operational readiness, perform service life extension analyses, propose design enhancements, develop repairs, and mitigate flight-safety risks. We will transition software toolkit into fleet overhaul facilities, fleet support teams, and OEM commercial markets. We expect to capture these markets by 2024 if the proposed effort is funded. This technology will also assist, automotive, and recreational vehicle industries that use advanced composite materials.
Keywords: Material Properties, Material Properties, digital image correlation, X-ray Computed Tomography, As-manufactured, Composite Materials, Data-driven Methods, Structures
