Robert J. Grandin

Surrogate Modeling of Ultrasonic Nondestructive Evaluation Simulations

Ultrasonic testing (UT) is used to detect internal flaws in materials or to characterize material properties. Computational simulations are an important part of the UT process. Fast models are essential for UT applications such as inverse design or model-assisted probability of detection. This paper presents investigations of using surrogate modeling techniques to create fast approximate models of UT simulator responses. In particular, we propose to use data-driven surrogate modeling techniques (kriging interpolation), and physics-based surrogate modeling techniques (space mapping), as well a mixture of the two approaches. These techniques are investigated for two cases involving UT simulations of metal components immersed in a water bath during the inspection process.

Implementation of automated 3D defect detection for low signal-to noise features in NDE data

The need for robust defect detection in NDE applications requires the identification of subtle, low-contrast changes in measurement signals usually in very noisy data. Most algorithms rarely perform at the level of a human inspector and often, as data sets are now routinely 10+ Gigabytes, require laborious manual inspection. We present two automated defect segmentation methods, simple threshold and a binomial hypothesis test, and compare effectiveness of these approaches in noisy data with signal to noise ratios at 1:1. The defect-detection ability of our algorithm will be demonstrated on a 3D CT volume, UT C-scan data, magnetic particle images, and using simulated data generated by XRSIM. The latter is a physics-based forward model useful in demonstrating the effectiveness of data processing approaches in a simulation which includes complex defect geometry and realistic measurement. These large data sets represent significant demands on compute resources and easily overwhelm typical PC platforms; however...

Model-assisted probability of detection of flaws in aluminum blocks using polynomial chaos expansions

Probability of detection (POD) is widely used for measuring reliability of nondestructive testing (NDT) systems. Typically, POD is determined experimentally, while it can be enhanced by utilizing physics-based computational models in combination withmodel-assisted POD (MAPOD) methods. With the development of advanced physics-basedmethods, such as ultrasonic NDTtesting, the empirical information,needed for POD methods, can bereduced. However, performing accurate numerical simulationscan be prohibitivelytimeconsuming, especially as part of stochastic analysis. In this work, stochastic surrogate models for computational physics-based measurement simulations are developed for cost savings of MAPOD methods while simultaneously ensuring sufficient accuracy. The stochastic surrogate is used to propagate the random input variables through thephysics-basedsimulation model to obtain the joint probability distribution of the output. The POD curves are then generated based on those results. Here, the stochastic surrogates are constructed using nonintrusive polynomial chaos (NIPC) expansions. In particular, the NIPC methods used are the quadrature, ordinary leastsquares (OLS), and least-angle regression sparse (LARS) techniques. The proposed approach is demonstrated on the ultrasonic testing simulation of a flat bottom hole flaw inanaluminum block. The results show that the stochastic surrogates have at least two orders of magnitude faster convergence on the statistics than direct Monte Carlo sampling (MCS). Moreover, the evaluation of the stochastic surrogate models is over three orders of magnitude faster than the underlying simulation modelfor this case,which is the UTSim2 model.

Surrogate modeling of ultrasonic simulations using data-driven methods

Ultrasonic testing (UT) is used to detect internal flaws in materials and to characterize material properties. In many applications, computational simulations are an important part of the inspection-design and analysis processes. Having fast surrogate models for UT simulations is key for enabling efficient inverse analysis and model-assisted probability of detection (MAPOD). In many cases, it is impractical to perform the aforementioned tasks in a timely manner using current simulation models directly. Fast surrogate models can make these processes computationally tractable. This paper presents investigations of using surrogate modeling techniques to create fast approximate models of UT simulator responses. In particular, we propose to integrate data-driven methods (here, kriging interpolation with variable-fidelity models to construct an accurate and fast surrogate model. These techniques are investigated using test cases involving UT simulations of solid components immersed in a water bath during the in...

Incorporation of Composite Defects from Ultrasonic NDE into CAD and FE Models

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 ...

An integrated framework for solid modeling and structural analysis of layered composites with defects

Abstract 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.

Evaluation of the fidelity of feature descriptor-based specimen tracking for automatic NDE data integration

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.

GPU-accelerated depth map generation for X-ray simulations of complex CAD geometries

Interactive x-ray simulations of complex computer-aided design (CAD) models can provide valuable insights for better interpretation of the defect signatures such as porosity from x-ray CT images. Generating the depth map along a particular direction for the given CAD geometry is the most compute-intensive step in x-ray simulations. We have developed a GPU-accelerated method for real-time generation of depth maps of complex CAD geometries. We preprocess complex components designed using commercial CAD systems using a custom CAD module and convert them into a fine user-defined surface tessellation. Our CAD module can be used by different simulators as well as handle complex geometries, including those that arise from complex castings and composite structures. We then make use of a parallel algorithm that runs on a graphics processing unit (GPU) to convert the finely-tessellated CAD model to a voxelized representation. The voxelized representation can enable heterogeneous modeling of the volume enclosed by the CAD model by assigning heterogeneous material properties in specific regions. The depth maps are generated from this voxelized representation with the help of a GPU-accelerated ray-casting algorithm. The GPU-accelerated ray-casting method enables interactive (> 60 frames-per-second) generation of the depth maps of complex CAD geometries. This enables arbitrarily rotation and slicing of the CAD model, leading to better interpretation of the x-ray images by the user. In addition, the depth maps can be used to aid directly in CT reconstruction algorithms.Interactive x-ray simulations of complex computer-aided design (CAD) models can provide valuable insights for better interpretation of the defect signatures such as porosity from x-ray CT images. Generating the depth map along a particular direction for the given CAD geometry is the most compute-intensive step in x-ray simulations. We have developed a GPU-accelerated method for real-time generation of depth maps of complex CAD geometries. We preprocess complex components designed using commercial CAD systems using a custom CAD module and convert them into a fine user-defined surface tessellation. Our CAD module can be used by different simulators as well as handle complex geometries, including those that arise from complex castings and composite structures. We then make use of a parallel algorithm that runs on a graphics processing unit (GPU) to convert the finely-tessellated CAD model to a voxelized representation. The voxelized representation can enable heterogeneous modeling of the volume enclosed by t...

Automated Construction of Layer-by-Layer Finite Element Sub-Models of Damaged Composites Based on NDE Data

Composite laminate structures are usually modeled as a shell in finite element analysis tools for strength and stiffness determination. However, modeling for fatigue or degradation analysis often needs to be performed with layer-by-layer solid models, but building these models for nontrivial geometries can be extremely difficult, especially when trying to represent realistic defects. This paper discusses how the process of generating layer-by-layer solid finite element models, including insertion of defects, can be automated. We have developed a tool, Delamo, to automate the construction of such models. The tool provides an interface to a commercial solid modeling kernel (ACIS) and a commercial finite element analysis package (ABAQUS). It allows the solid model and finite element model to be built in parallel, layer by layer, starting with a mold, following the same assembly steps as the physical laminate. The bonding step determines the boundary conditions to be applied in the finite element model. Delaminations, determined from nondestructive evaluation (NDE) data can be inserted between layers as needed and are represented as unbonded regions. Potential delamination growth regions can be modeled with a cohesive layer or cohesive boundary condition. Fiber breakage in a layer will be represented by an internal boundary. Based on a mold and a sequence of layer construction and bonding instructions, the tool generates both a solid model and a Python script for ABAQUS that will generate a complete finite element model based on that solid model.

Meso-scale imaging of composite materials

The performance of composite materials is controlled by the interaction between the individual components as well as the mechanical characteristics of the components themselves. Geometric structure on the meso-scale, where the length-scales are of the same order as the material granularity, plays a key role in controlling material performance and having a quantitative means of characterizing this structure is crucial in developing our understanding of NDE technique signatures of early damage states. High-resolution computed tomography (HRCT) provides an imaging capability which can resolve these structures for many composite materials. Coupling HRCT with three-dimensional physics-based image processing enables quantitative characterization of the meso-scale structure. Taking sequences of these damage states provides a means to structurally observe the damages evolution. We will discuss the limits of present 3DCT capability and challenges for improving this means to rapidly generate structural information of a composite and of the damage. In this presentation we will demonstrate the imaging capability of HRCT.