Automated Construction and Insertion of Layer-by Layer Finite Element Sub-Models of Damaged Composites

Finite element models of composite structures are generally shell-based and modeled at the laminate level. More detailed layer-by-layer lamina-level models are sometimes needed for representing joints or for modeling defect growth processes. We describe a method and toolkit for automating the creation and insertion of layerby-layer finite element sub-models of composite laminate. We focus in particular on representing damage captured from nondestructive evaluation (NDE) measurements. The method is based on scripting existing simulation and solid modeling tools (ABAQUS and ACIS). It works even on complicated, curved CAD models. The submodel location is identified by the intersection of a cylinder with the structure. We then execute a series of instructions to generate a new shell with the submodel region removed, generate the layer-by-layer submodel, and bond together the layers and models with desired boundary conditions and defects. The instructions represent the steps of lamination and bonding for creating the composite. The output of the method includes CAD models of the new shell and each lamina within the submodel, and a Python script for ABAQUS that will load the CAD models, bond them together, and apply the specified boundary conditions

Rapid B-rep model preprocessing for immersogeometric analysis using analytic surfaces

Computational fluid dynamics (CFD) simulations of flow over complex objects have been performed traditionally using fluid-domain meshes that conform to the shape of the object. However, creating shape conforming meshes for complicated geometries such as automobiles require extensive geometry preprocessing. This process is usually tedious and requires modifying the geometry, including specialized operations such as defeaturing and filling of small gaps.Hsu et al. (2016) developed a novel immersogeometric fluid-flow method that does not require the generation of a boundary-fitted mesh for the fluid domain. However, their method used the NURBS parameterization of the surfaces for generating the surface quadrature points to enforce the boundary conditions, which required the B-rep model to be converted completely to NURBS before analysis can be performed. This conversion usually leads to poorly parameterized NURBS surfaces and can lead to poorly trimmed or missing surface features. In addition, converting simple geometries such as cylinders to NURBS imposes a performance penalty since these geometries have to be dealt with as rational splines. As a result, the geometry has to be inspected again after conversion to ensure analysis compatibility and can increase the computational cost. In this work, we have extended the immersogeometric method to generate surface quadrature points directly using analytic surfaces. We have developed quadrature rules for all four kinds of analytic surfaces: planes, cones, spheres, and tori. We have also developed methods for performing adaptive quadrature on trimmed analytic surfaces. Since analytic surfaces have frequently been used for constructing solid models, this method is also faster to generate quadrature points on real-world geometries than using only NURBS surfaces. To assess the accuracy of the proposed method, we perform simulations of a benchmark problem of flow over a torpedo shape made of analytic surfaces and compare those to immersogeometric simulations of the same model with NURBS surfaces. We also compare the results of our immersogeometric method with those obtained using boundary-fitted CFD of a tessellated torpedo shape, and quantities of interest such as drag coefficient are in good agreement. Finally, we demonstrate the effectiveness of our immersogeometric method for high-fidelity industrial scale simulations by performing an aerodynamic analysis of a truck that has a large percentage of analytic surfaces. Using analytic surfaces over NURBS avoids unnecessary surface type conversion and significantly reduces model-preprocessing time, while providing the same accuracy for the aerodynamic quantities of interest

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

NURBS-based microstructure design for organic photovoltaics

The microstructure – spatial distribution of electron donor and acceptor domains – plays an important role in determining the photo current in thin film organic solar cells (OSCs). Optimizing the microstructure can lead to higher photo current generation, and is an active area of experimental research. There has been recent progress in framing OSC microstructure design as a computational design problem. However, most current approaches to microstructure optimization are based on volumetric distribution of material, which makes the design space very large. In contrast, we frame the microstructure design optimization problem in terms of designing the interface between the donor and acceptor regions, and thus pose it as a surface representation and optimization problem. This results in substantially reduced number of design variables, thus enabling use of standard optimization tools. In this work, we address the efficient design of OSC microstructure by using surface and curve modeling techniques to model the donor–acceptor interface, and use meta-heuristic, gradient-free optimization techniques to optimize the microstructure for maximum short circuit current generation. Our modeling framework consists of three major components: (1) geometric modeling of OSC microstructure that uses Non-Uniform Rational B-spline (NURBS) curves and surfaces to construct the free-form donor–acceptor interface, (2) photo-current generation modeling that uses a parallel, finite-element based exciton–drift–diffusion (XDD) model, and (3) optimization that utilizes genetic algorithms (GA) to optimize the OSCs microstructure via exploration of the NURBS representation. We apply these methods for the optimization of both 2D and 3D microstructures. Results show substantial improvement in current density compared to the bulk-heterojunction microstructures. These results provide promising microstructures for experimental groups to fabricate. The proposed surface representation approach seems to be a promising approach for interface design in engineered systems

Towards Foundational AI Models for Additive Manufacturing: Language Models for G-Code Debugging, Manipulation, and Comprehension

3D printing or additive manufacturing is a revolutionary technology that enables the creation of physical objects from digital models. However, the quality and accuracy of 3D printing depend on the correctness and efficiency of the G-code, a low-level numerical control programming language that instructs 3D printers how to move and extrude material. Debugging G-code is a challenging task that requires a syntactic and semantic understanding of the G-code format and the geometry of the part to be printed. In this paper, we present the first extensive evaluation of six state-of-the-art foundational large language models (LLMs) for comprehending and debugging G-code files for 3D printing. We design effective prompts to enable pre-trained LLMs to understand and manipulate G-code and test their performance on various aspects of G-code debugging and manipulation, including detection and correction of common errors and the ability to perform geometric transformations. We analyze their strengths and weaknesses for understanding complete G-code files. We also discuss the implications and limitations of using LLMs for G-code comprehension

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

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

A Deep Learning Framework for Design and Analysis of Surgical Bioprosthetic Heart Valves

Bioprosthetic heart valves (BHVs) are commonly used as heart valve replacements but they are prone to fatigue failure; estimating their remaining life directly from medical images is difficult. Analyzing the valve performance can provide better guidance for personalized valve design. However, such analyses are often computationally intensive. In this work, we introduce the concept of deep learning (DL) based finite element analysis (DLFEA) to learn the deformation biomechanics of bioprosthetic aortic valves directly from simulations. The proposed DL framework can eliminate the time-consuming biomechanics simulations, while predicting valve deformations with the same fidelity. We present statistical results that demonstrate the high performance of the DLFEA framework and the applicability of the framework to predict bioprosthetic aortic valve deformations. With further development, such a tool can provide fast decision support for designing surgical bioprosthetic aortic valves. Ultimately, this framework could be extended to other BHVs and improve patient care

Direct Immersogeometric Fluid Flow and Heat Transfer Analysis of Objects Represented by Point Clouds

Immersogeometric analysis (IMGA) is a geometrically flexible method that enables one to perform multiphysics analysis directly using complex computer-aided design (CAD) models. In this paper, we develop a novel IMGA approach for simulating incompressible and compressible flows around complex geometries represented by point clouds. The point cloud object's geometry is represented using a set of unstructured points in the Euclidean space with (possible) orientation information in the form of surface normals. Due to the absence of topological information in the point cloud model, there are no guarantees for the geometric representation to be watertight or 2-manifold or to have consistent normals. To perform IMGA directly using point cloud geometries, we first develop a method for estimating the inside-outside information and the surface normals directly from the point cloud. We also propose a method to compute the Jacobian determinant for the surface integration (over the point cloud) necessary for the weak enforcement of Dirichlet boundary conditions. We validate these geometric estimation methods by comparing the geometric quantities computed from the point cloud with those obtained from analytical geometry and tessellated CAD models. In this work, we also develop thermal IMGA to simulate heat transfer in the presence of flow over complex geometries. The proposed framework is tested for a wide range of Reynolds and Mach numbers on benchmark problems of geometries represented by point clouds, showing the robustness and accuracy of the method. Finally, we demonstrate the applicability of our approach by performing IMGA on large industrial-scale construction machinery represented using a point cloud of more than 12 million points.Comment: 30 pages + references; Accepted in Computer Methods in Applied Mechanics and Engineerin

Latent Diffusion Models for Structural Component Design

Recent advances in generative modeling, namely Diffusion models, have revolutionized generative modeling, enabling high-quality image generation tailored to user needs. This paper proposes a framework for the generative design of structural components. Specifically, we employ a Latent Diffusion model to generate potential designs of a component that can satisfy a set of problem-specific loading conditions. One of the distinct advantages our approach offers over other generative approaches, such as generative adversarial networks (GANs), is that it permits the editing of existing designs. We train our model using a dataset of geometries obtained from structural topology optimization utilizing the SIMP algorithm. Consequently, our framework generates inherently near-optimal designs. Our work presents quantitative results that support the structural performance of the generated designs and the variability in potential candidate designs. Furthermore, we provide evidence of the scalability of our framework by operating over voxel domains with resolutions varying from $32^3$ to $128^3$. Our framework can be used as a starting point for generating novel near-optimal designs similar to topology-optimized designs