Fiber-reinforced composites are widely used in aerospace industry due to their combined properties of high strength and low weight. However, owing to their complex structure, it is difficult to assess the impact of manufacturing defects and service damage on their residual life. While, ultrasonic testing (UT) is the preferred NDE method to identify the presence of defects in composites, there are no reasonable ways to model the damage and evaluate the structural integrity of composites. We have developed an automated framework to incorporate flaws and known composite damage automatically into a finite element analysis (FEA) model of composites, ultimately aiding in accessing the residual life of composites and make informed decisions regarding repairs. The framework can be used to generate a layer-by-layer 3D structural CAD model of the composite laminates replicating their manufacturing process. Outlines of structural defects, such as delaminations, are automatically detected from UT of the laminate and are incorporated into the CAD model between the appropriate layers. In addition, the framework allows for direct structural analysis of the resulting 3D CAD models with defects by automatically applying the appropriate boundary conditions. In this paper, we show a working proof-of-concept for the composite model builder with capabilities of incorporating delaminations between laminate layers and automatically preparing the CAD model for structural analysis using a FEA software.

This research addresses inspection location tracking in the field of nondestructive evaluation (NDE) using a computer vision technique to determine the position and orientation of typical NDE equipment in a test setup. The objective is the tracking accuracy for typical NDE equipment to facilitate automatic NDE data integration. Since the employed tracking technique relies on surface curvatures of an object of interest, the accuracy can be only experimentally determined. We work with flash-thermography and conducted an experiment in which we tracked a specimen and a thermography flash hood, measured the spatial relation between both, and used the relation as input to map thermography data onto a 3D model of the specimen. The results indicate an appropriate accuracy, however, unveiled calibration challenges.

Laminated fiber-reinforced polymer (FRP) composites are widely used in aerospace and automotive industries due to their combined properties of high strength and low weight. However, owing to their complex structure, it is difficult to assess the impact of manufacturing defects and service damage on their residual life. Non-destructive evaluation (NDE) of composites using ultrasonic testing (UT) can identify the presence of defects. However, manually incorporating the damage in a CAD model of a multi-layered composite structure and evaluating its structural integrity is a tedious process. We have developed an automated framework to create a layered 3D CAD model of a composite structure and automatically preprocess it for structural finite element (FE) analysis. In addition, we can incorporate flaws and known composite damage automatically into this CAD model. The framework generates a layer-by-layer 3D structural CAD model of the composite laminate, replicating its manufacturing process. The framework can create non-trivial composite structures such as those that include stiffeners. Outlines of structural defects, such as delaminations detected using UT of the laminate, are incorporated into the CAD model between the appropriate layers. The framework is also capable of incorporating fiber/matrix cracking, another common defect observed in fiber-reinforced composites. Finally, the framework can preprocess the resulting 3D CAD models with defects for direct structural analysis by automatically applying the appropriate boundary conditions. In this paper, we show a working proof-of-concept of the framework with capabilities of creating composite structures with stiffeners, incorporating delaminations between the composite layers, and automatically preprocessing the CAD model for finite element structural analysis. The framework will ultimately aid in accurately assessing the residual life of the composite and making informed decisions regarding repairs.

Nondestructive evaluation (NDE) techniques are widely used to detect flaws in critical components of systems like aircraft engines, nuclear power plants and oil pipelines in order to prevent catastrophic events. Many modern NDE systems generate image data. In some applications an experienced inspector performs the tedious task of visually examining every image to provide accurate conclusions about the existence of flaws. This approach is labor-intensive and can cause misses due to operator ennui. Automated evaluation methods seek to eliminate human-factors variability and improve throughput. Simple methods based on peak amplitude in an image are sometimes employed and a trained-operator-controlled refinement that uses a dynamic threshold based on signal-to-noise ratio (SNR) has also been implemented. We develop an automated and optimized detection procedure that mimics these operations. The primary goal of our methodology is to reduce the number of images requiring expert visual evaluation by filtering out images that are overwhelmingly definitive on the existence or absence of a flaw. We use an appropriate model for the observed values of the SNR-detection criterion to estimate the probability of detection. Our methodology outperforms current methods in terms of its ability to detect flaws.