Generation Of Finite Element Models Research Articles

Streamlining FE and BIM Modeling for Historic Buildings with Point Cloud Transformation

ABSTRACT In this paper, a convenient workflow to obtain a simplified lightweight IFC description of a dense point cloud describing a historic building is proposed. The proposed procedure relies on slicing of the point cloud that aims at reconstructing the geometry of the solid. The resulting slices can then be easily employed in the generation of finite element (FE) and architectural building information modeling (BIM) models. In particular, the FE model generation procedure guarantees the obtainment of a conforming solid FE mesh ready to be used for structural purposes. Contextually, the architectural BIM model generation is achieved by slices extrusion to obtain the overall volume of the building. Then, the assembly of independent slice-based meshes is subjected to re-topology to obtain the overall watertight bounding surface of the building. A real historic structure is here used to show the effectiveness of the FE and BIM model generation procedures. Finally, the BIM model generation of an actual historic structure is carried out through the proposed workflow.

Biomechanical behavior of three maxillary expanders in cleft lip and palate: a finite element study.

This study evaluated the stress distribution in the dentoalveolar and palatal bone structures during maxillary expansion in a 17-year-old male patient with bilateral cleft lip and palate (BCLP) using expanders with dental (HYRAX) and skeletal anchorage (MARPE). For the generation of the specific finite element models, cone-beam computed tomography was used, and the DICOM files were exported to Mimics 3-Matic (Materialise) and Patran (MSC Software) software. Three specific three-dimensional models were generated: A) HYRAX: conventional four-banded hyrax screw (9 mm); B) MARPE-DS: 3 miniscrews (1.8 mm diameter - 5.4 mm length) and four-banded dental anchorage; and C) MARPE-NoDS: 3 miniscrews without dental anchorage. Maxillary expansion was simulated by activating the expanders transversely 1 mm on the "X" axis. HYRAX resulted in higher levels of deformation predominantly in the dentoalveolar region. MARPE-DS showed stress in the dentoalveolar region and mainly in the center of the palatal region, at approximately 4,000 με. MARPE-NoDS exhibited evident stress only in the palatal region. High stress levels in the root anchoring teeth were observed for HYRAX and MARPE-DS. In contrast, MARPE-NoDS cause stress on the tooth structure. The stress distribution from the expanders used in the BLCP showed asymmetric expansive behavior. During the initial activation phase of expansion, the HYRAX and MARPE-DS models produced similarly high strain at the dentoalveolar structures and upper posterior teeth displacement. The MARPE-NoDS model showed restricted strain on the palate.

Automatic generation of 3D micromechanical finite element model with periodic boundary conditions to predict elastic properties of bamboo fibre-reinforced composites

This study presents an automatic algorithm to generate a three-dimensional (3D) micromechanical finite element (FE) model to predict elastic behaviours of biocomposites reinforced with unidirectional (UD) continuous natural fibres such as bamboo fibres. The developed algorithm constructs a virtual FE framework by generating stochastic Representative Volume Elements (RVEs) of fibre-reinforced composites incorporating random fibre arrangement and random assignment of fibre radii. It also applies various loading scenarios and periodic boundary conditions to simulate elastic behaviours accurately. The algorithm generates 3D periodic RVEs by introducing boundary and corner fibres. All the 3D RVE models, representing different fibre-reinforced composites, various loading cases, and periodic boundary conditions, are automatically created using Python scripting with the Abaqus FE solver. The accuracy of the algorithm and virtual FE test model is validated through comparisons with analytical models and experimental results. The effects of different types of fibre radius assignment, fibre volume fraction, and fibre periodicity on the prediction of effective elastic properties are investigated. The study also establishes correlations between all elastic constants of bamboo fibre-reinforced composites and the fibre volume fraction.

Automatic generation of subject-specific finite element models of the spine from magnetic resonance images.

The generation of subject-specific finite element models of the spine is generally a time-consuming process based on computed tomography (CT) images, where scanning exposes subjects to harmful radiation. In this study, a method is presented for the automatic generation of spine finite element models using images from a single magnetic resonance (MR) sequence. The thoracic and lumbar spine of eight adult volunteers was imaged using a 3D multi-echo-gradient-echo sagittal MR sequence. A deep-learning method was used to generate synthetic CT images from the MR images. A pre-trained deep-learning network was used for the automatic segmentation of vertebrae from the synthetic CT images. Another deep-learning network was trained for the automatic segmentation of intervertebral discs from the MR images. The automatic segmentations were validated against manual segmentations for two subjects, one with scoliosis, and another with a spine implant. A template mesh of the spine was registered to the segmentations in three steps using a Bayesian coherent point drift algorithm. First, rigid registration was applied on the complete spine. Second, non-rigid registration was used for the individual discs and vertebrae. Third, the complete spine was non-rigidly registered to the individually registered discs and vertebrae. Comparison of the automatic and manual segmentations led to dice-scores of 0.93-0.96 for all vertebrae and discs. The lowest dice-score was in the disc at the height of the implant where artifacts led to under-segmentation. The mean distance between the morphed meshes and the segmentations was below 1mm. In conclusion, the presented method can be used to automatically generate accurate subject-specific spine models.

Optimal sensor placement for structural parameter identification of bridges with modeling uncertainties

The uncertainties in the numerical models used for the optimal sensor placement (OSP) studies of civil infrastructures, specifically bridges, considerably affect the results. These effects can be more prominent if the modeling uncertainties are of a kind that significantly alters the mode shapes of the structure, such as the boundary conditions of the model. Yet, these effects on the results of OSP analysis remain unexplored, and there are no available methodologies to address all types of model uncertainties in OSP for civil infrastructures. This research presents a new framework to determine the optimal sensor locations to identify the modal properties of bridges under severe modeling uncertainties and its application on a railway bridge. The framework includes finite element model generation and a sensitivity study to select the most influential parameters that change the dynamic response of the bridge. The selected parameters are used in Monte Carlo simulations, and the results enable quantifying the relative amount of information presented at the candidate sensor locations. This information is combined with the spatial position of the candidate sensor locations, and a hierarchical clustering algorithm is used to obtain the optimal sensor locations and the number of sensors. In addition, the OSP analysis is carried out using the Effective Independence method to contribute to the state-of-the-art literature that investigates the uncertainties in OSP for civil infrastructures and uses this method.

Automated image-based generation of finite element models for masonry buildings

To predict the response of masonry buildings to various types of loads, engineers use finite element models, specifically solid-element and macro-element models. For predicting masonry responses to seismic events in particular, equivalent frame models—a subcategory of macro-element models—are a common choice because of their low computational cost. However, an existing bottleneck in modeling pipelines is generating the geometry of the model, which is currently a slow and laborious process that is done manually using computer-aided design tools. In this paper, we address this by automating the modelling process using recent advancements in computer vision and machine learning. We present an image-based end-to-end pipeline that automatically generates finite element meshes for solid-element and equivalent-frame models of the outer walls of free-standing historical masonry buildings. As the input, our framework requires RGB images of the buildings that are processed using structure-from-motion algorithms, which create 3D geometries, and convolutional neural networks, which segment the openings and their corners. These layers are then combined to generate level of detail models. We tested our pipeline on structures with irregular surface geometries and opening layouts. While generating the solid element mesh from the level of detail model is straightforward, generating equivalent frame models required algorithms for segmenting the façade and the meshing. Experts in the field analyzed the generated equivalent frame models and determined them to be useful for numerical modeling. These finite element geometries will be invaluable for future predictions of the seismic response of damaged and undamaged buildings. The codes and dataset are publicly available for future studies and benchmarking (https://github.com/eesd-epfl/FEM_buildings and https://doi.org/10.5281/zenodo.8094306).

Automatic ply detection and finite element model generation for composite laminates

This paper presents a novel methodology to detect and isolate individual ply information contained within a relatively low-resolution cross-section image of thick composite laminate specimens. The proposed method can process laminate sample images and construct detailed geometric models in a fast and automated manner with minimal user interaction. The finite element models can be used directly for structural and strength simulations to analyse the effect of waviness defects. The algorithm processes the greyscale sample image and splits it into multiple slices. The initial starting points for each ply were identified by analysing the pixel brightness of the image. The pixel brightness variation was used to identify the different plies in all image slices and a list of possible ply centreline coordinate is generated. The ply centreline points are grouped and connected by selecting the points with minimal distance to the previous one in the ply. A finite element mesh is created for each ply by creating a boundary at the midpoint between two adjacent plies. The geometric information of the isolated plies is then used to create structured finite element models using an in-house meshing algorithm.

An unified CAD/CAE/VR tool for ship structure design and evaluation based on multi-domain feature mapping

Computer-aided design (CAD), computer-aided engineering (CAE), and virtual reality (VR) are three distinct disciplines. For the collaborative design and interactive evaluation of ship structures, the main obstacle lies in the large differences in model data structures. In this regard, a unified tool for ship structure design/analysis/virtual evaluation based on Multi-Domain Feature Mapping (MDFM) is proposed. The multi-layer transmission and matching mechanism between data, information and knowledge established by Extensible Markup Language (XML) can realize the feature association among design, analysis and evaluation domain, so that CAD, CAE and VR become an organic entity. In the virtual scene, a visual expression strategy of CAD model and FEA data based on Unified Mesh Model (UMM) is proposed. The advantage of this unified tool lies in the intelligent generation of finite element models, as well as the virtual visualization and model reconstruction and evaluation of models with different topologies. Intelligent finite element (FE) meshing and analysis applications integrating knowledge templates represent a major subset of unified CAD/CAE/VR tools for structural models from CAD systems. The structure and data flow management of this unified tool is based on a common design process, which allows designers to link anticipated scenarios with proposed engineering changes. Finally, the effectiveness of the proposed tool is demonstrated by engineering examples.

An Efficient Approach for Parametric Modeling and Prediction of the Hollow Blade Manufacture Shape

In recent years, hollow fan blades have been widely used to meet the requirements of aeroengines for lightweight and better performance. However, the hollow fan blade will change from the manufacture shape to the operating shape with large deformations during operation. This deviation, if neglected, will lead to deceptive results for structural and aerodynamic analysis. However, the existing methods have low prediction accuracy or require a lot of calculation in case of large deformations. In this paper, an iterative method for parametric modeling, automatic generation of finite element model, and hot-to-cold analysis of an H-shaped hollow fan blade are proposed. The accuracy and efficiency of the traditional uncoupling (UCM) and weak coupling methods (WCS), as well as the proposed strong–weak coupling method (SWCS), are compared with the strong coupling method (SCU) as a reference. Results show that improvements in the prediction accuracy can be made by the SWCS method, and the error of the maximum blade deformation is 2.3%, while the error of the UCM method and WCS method is 30% and 12%, respectively. An excellent agreement can be observed between the SWCS and SCU methods in the whole blade height with errors of 2%~5%, and the calculation time of the SWCS method is only 2.5% of the SCU method, which is reduced from 7200 min to 180 min, making it possible to conduct the hot-to-cold analysis at the design stage.

Lumbar intervertebral disc segmentation for computer modeling and simulation

Background and objectiveThe present work had as its main objective the development of a method for localizing and automatically segmenting lumbar intervertebral discs (IVD) in 3D from magnetic resonance imaging (MRI), with the goal of supporting the generation of finite element (FE) models from actual lumbar spine anatomy, by providing accurate and personalized information on the shape of the patient's IVD. The extension of the method to allow performing separate segmentations of the IVD's two main structures – annulus fibrosus (AF) and nucleus pulposus (NP) – as well as automatically detecting degenerated IVD where this distinction is no longer possible was also an objective of the work. MethodsThe method presented here evolves from 2D segmentations in the sagittal profile using Gabor filters towards 3D segmentations. It works by detecting the spine curves and intensity regions corresponding to IVD. As so, the 2D method from Zhu et al. (2013) was partially implemented, modified and adapted to 3D use, and then tested with eight spines from two separated online datasets. The 3D adaptation was achieved by using vertebral body segmentation masks to approximate the shape of the vertebrae and to adjust the spine curves accordingly. ResultsThe method showed average values of 85%, 83% and 96% for the Dice coefficient, sensitivity and specificity, respectively. The method correctly identified 65 of 68 (96%) IVD as either healthy or degenerated. The method's Dice coefficient is within the range of existing 3D IVD segmentation methods in the literature (81–92%). The method took on average 6–7 s to perform a full 3D segmentation, which is well within the range of the existing methods (2 s – 19 min). ConclusionsThe developed method can be used to generate accurate 3D models of the IVD based on MRI, with AF/NP distinction and detection of marked degeneration by comparing each IVD with the remaining spine levels. Further work shall improve the method towards distinguishing between specific levels of degeneration for clinically oriented FE modeling.

Experimental validation of a subject-specific finite element model of lumbar spine segment using digital image correlation.

Pathologies such as cancer metastasis and osteoporosis strongly affect the mechanical properties of the vertebral bone and increase the risk of fragility fractures. The prediction of the fracture risk with a patient-specific model, directly generated from the diagnostic images of the patient, could help the clinician in the choice of the correct therapy to follow. But before such models can be used to support any clinical decision, their credibility must be demonstrated through verification, validation, and uncertainty quantification. In this study we describe a procedure for the generation of such patient-specific finite element models and present a first validation of the kinematics of the spine segment. Quantitative computed tomography images of a cadaveric lumbar spine segment presenting vertebral metastatic lesions were used to generate the model. The applied boundary conditions replicated a specific experimental test where the spine segment was loaded in compression-flexion. Model predictions in terms of vertebral surface displacements were compared against the full-field experimental displacements measured with Digital Image Correlation. A good agreement was obtained from the local comparison between experimental data and simulation results (R2 > 0.9 and RMSE% <8%). In conclusion, this work demonstrates the possibility to apply the developed modelling pipeline to predict the displacement field of human spine segment under physiological loading conditions, which is a first fundamental step in the credibility assessment of these clinical decision-support technology.

X-ray CT based multi-layer unit cell modeling of carbon fiber-reinforced textile composites: Segmentation, meshing and elastic property homogenization

Generation of realistic finite element method (FEM) geometry of a textile composite material at tow scale remains a challenging stage of material modeling. In this paper, a FE model generation approach is introduced, based on micro-Computed Tomography (µ-CT) images of a carbon fiber reinforced textile composite with multiple layer orientations. For the specified laminate layup made of plain weave fabric layers, a tow instance segmentation method based on deep learning was proposed. The fiber local orientation field was extracted from the tow centerlines approximation. For the image-based finite element meshing, a material interface reconstruction algorithm was proposed. The algorithm leads to a conformal and smooth finite element (FE) mesh with high-quality elements. FE models with voxel and tetrahedral meshes considering different approximations of the material orientation field were generated and used for numerical homogenization of the composite material's elastic properties.

Femoral Bone Strength Prediction Using Isotopological B-Spline-Transformed Meshes

Finite element (FE) analysis can predict proximal human femoral strength. Automated meshing and identifying subregions with high relevance for strength prediction could reduce the laborious modeling process. Mesh morphing based on free-form registration provides a high level of automation and inherently creates isotopological meshes. The goals of this study were to investigate if FE models based on free-form transformed meshes predict experimental femoral strength as well as manually created FE models and to identify regions and parameters with highest correlation to femoral strength. Subject-specific meshes and FE models were created from a set of quantitative CT images (QCT) using a B-Spline registration-based algorithm. Correlation of FE-predicted bone strength and local parameters with experimental bone strength were investigated. FE models based on transformed meshes closely resembled manually created counterparts, with equally strong correlations with experimental bone strength (R2=0.81 vs. R2=0.80). The regional analysis showed strong correlations (0.6&lt;R2&lt;0.7) of experimental strength with local parameters. No subregion or parameter lead to stronger correlation than FE predicted bone strength. B-spline-transformed meshes can be used to create FE models, able to predict femoral bone strength and simplify FE model generation. They can be used to reveal relations of local parameters with failure load.

Experimental DVC validation of heterogeneous micro finite element models applied to subchondral trabecular bone of the humeral head.

Subchondral trabecular bone (STB) undergoes adaptive changes during osteoarthritic (OA) disease progression. These changes alter both the mineralization patterns and structure of bone and may contribute to variations in the mechanical properties. Similarly, when images are downsampled - as is often performed in micro finite element model (microFEM) generation - the morphological and mineralization patterns may further alter the mechanical properties due to partial volume effects. MicroFEMs accounting for material heterogeneity can account for these tissue variations, but no studies have validated these with robust full-field testing methods. As such, this study compared homogeneous and heterogeneous microFEMs to experimentally loaded trabecular bone cores from the humeral head combined with digital volume correlation (DVC). These microFEMs were used to compare apparent mechanical properties between normal and OA STB. Morphological and mineralization patterns between groups were also compared. There were no significant differences in tissue or bone mineral density between groups. The only significant differences in morphometric parameters were in trabecular thickness between groups. There were no significant differences in linear regression parameters between normal and OA STB apparent mechanical properties estimated using heterogeneous microFEMs with an element-wise bilinear elastic-plastic constitutive model. Clinical significance: Validated heterogeneous microFEMs applied to STB of the humeral head have the potential to significantly improve our understanding of mechanical variations in the bone that occur during OA progression.

Stress distribution in the bonobo (Pan paniscus) trapeziometacarpal joint during grasping.

The primate thumb plays a central role in grasping and the basal trapeziometacarpal (TMC) joint is critical to its function. The TMC joint morphology varies across primates, yet little is known about form-function interaction within in the TMC joint. The purpose of this study was to investigate how stress distributions within the joint differ between five grasping types commonly employed by bonobos (Pan paniscus). Five cadaveric bonobo forearms were CT scanned in five standardized positions of the hand as a basis for the generation of parametric finite element models to compare grasps. We have developed a finite element analysis (FEA) approach to investigate stress distribution patterns in the TMC joint associated with each grasp type. We hypothesized that the simulated stress distributions for each position would correspond with the patterns expected from a saddle-shaped joint. However, we also expected differences in stress patterns arising from instraspecific variations in morphology. The models showed a high agreement between simulated and expected stress patterns for each of the five grasps (86% of successful simulations), while partially (52%) and fully (14%) diverging patterns were also encountered. We identified individual variations of key morphological features in the bonobo TMC joint that account for the diverging stress patterns and emphasized the effect of interindividual morphological variation on joint functioning. This study gives unprecedented insight in the form-function interactions in the TMC joint of the bonobo and provides an innovative FEA approach to modelling intra-articular stress distributions, a valuable tool for the study of the primate thumb biomechanics.

Numerical characterization of local and global non-uniformities in the load distribution in ball screws

Load distribution in ball screws is a representation of the ball contact stress, and it is fundamental to understanding the behavior of these machine elements. This work aims to conduct a multi-variable analysis of the load distribution in ball screws. For this purpose, a numerical tool is developed for the generation and calculation of ball screw finite element (FE) models, which has been validated against the state of the art. This tool is based on the combination of an analytical contact model and the use of high-order FE models for the analysis of the load distribution of ball screws and stands out for its accuracy (less than 1% error against high-order FE models), adaptability, versatility (models are generated with more than 20 design variables and they can be introduced as components in larger models) and efficiency (being the computational time 1.25% of that of a high-order FE models) with respect to other existing models. Many different design variables (number of start threads, pitch, contact angle, ball size, slenderness and load arrangement) are studied in order to obtain a general characterization of the morphology of the load distribution in ball screws. Among them, the most influential variables on the load distribution and therefore on the structural behavior of ball screws are, load arrangement (with ratio r variations of up to 25% on the same ball screw) and slenderness (with ratio variations of up to 13% on ball screws with two turns of difference). The two most characteristic features, the non-uniformity at a local and global level are identified, along with as the possible causes of their appearance and the consequences that they may cause.

A high-fidelity crystal-plasticity finite element methodology for low-cycle fatigue using automatic electron backscatter diffraction scan conversion: Application to hot-rolled cobalt–chromium alloy

The common approach to crystal-plasticity finite element modeling for load-bearing prediction of metallic structures involves the simulation of simplified grain morphology and substructure detail. This paper details a methodology for predicting the structure–property effect of as-manufactured microstructure, including true grain morphology and orientation, on cyclic plasticity, and fatigue crack initiation in biomedical-grade CoCr alloy. The methodology generates high-fidelity crystal-plasticity finite element models, by directly converting measured electron backscatter diffraction metal microstructure grain maps into finite element microstructural models, and thus captures essential grain definition for improved microstructure–property analyses. This electron backscatter diffraction-based method for crystal-plasticity finite element model generation is shown to give approximately 10% improved agreement for fatigue life prediction, compared with the more commonly used Voronoi tessellation method. However, the added microstructural detail available in electron backscatter diffraction–crystal-plasticity finite element did not significantly alter the bulk stress–strain response prediction, compared to Voronoi tessellation–crystal-plasticity finite element. The new electron backscatter diffraction-based method within a strain-gradient crystal-plasticity finite element model is also applied to predict measured grain size effects for cyclic plasticity and fatigue crack initiation, and shows the concentration of geometrically necessary dislocations around true grain boundaries, with smaller grain samples exhibiting higher overall geometrically necessary dislocations concentrations. In addition, minimum model sizes for Voronoi tessellation–crystal-plasticity finite element and electron backscatter diffraction–crystal-plasticity finite element models are proposed for cyclic hysteresis and fatigue crack initiation prediction.

Modeling the Cervical Spine Unit: A Finite Element Approach

The objective of this research is to address the new advances in the generation of finite element models of the human cervical spine....

Automated Pipeline to Generate Anatomically Accurate Patient-Specific Biomechanical Models of Healthy and Pathological FSUs.

State-of-the-art preoperative biomechanical analysis for the planning of spinal surgery not only requires the generation of three-dimensional patient-specific models but also the accurate biomechanical representation of vertebral joints. The benefits offered by computational models suitable for such purposes are still outweighed by the time and effort required for their generation, thus compromising their applicability in a clinical environment. In this work, we aim to ease the integration of computerized methods into patient-specific planning of spinal surgery. We present the first pipeline combining deep learning and finite element methods that allows a completely automated model generation of functional spine units (FSUs) of the lumbar spine for patient-specific FE simulations (FEBio). The pipeline consists of three steps: (a) multiclass segmentation of cropped 3D CT images containing lumbar vertebrae using the DenseVNet network, (b) automatic landmark-based mesh fitting of statistical shape models onto 3D semantic segmented meshes of the vertebral models, and (c) automatic generation of patient-specific FE models of lumbar segments for the simulation of flexion-extension, lateral bending, and axial rotation movements. The automatic segmentation of FSUs was evaluated against the gold standard (manual segmentation) using 10-fold cross-validation. The obtained Dice coefficient was 93.7% on average, with a mean surface distance of 0.88 mm and a mean Hausdorff distance of 11.16 mm (N = 150). Automatic generation of finite element models to simulate the range of motion (ROM) was successfully performed for five healthy and five pathological FSUs. The results of the simulations were evaluated against the literature and showed comparable ROMs in both healthy and pathological cases, including the alteration of ROM typically observed in severely degenerated FSUs. The major intent of this work is to automate the creation of anatomically accurate patient-specific models by a single pipeline allowing functional modeling of spinal motion in healthy and pathological FSUs. Our approach reduces manual efforts to a minimum and the execution of the entire pipeline including simulations takes approximately 2 h. The automation, time-efficiency and robustness level of the pipeline represents a first step toward its clinical integration.

Influence of pelvic shape on strain patterns: A computational analysis using finite element mesh morphing techniques

The pelvis functions to transmit upper body loads to the lower limbs and is critical in human locomotion. Semi-automated, finite element (FE) morphing techniques eliminate the need for segmentation and have shown to accelerate the generation of multiple specimen-specific pelvic FE models to enable the study of pelvic mechanical behaviour. The purpose of this research was to produce simulated human pelvic FE models representing android, gynecoid, anthropoid and platypelloid morphologies and to isolate differences in strain patterns due to anatomic shape under physiologic loading. Using five initially generated specimen-specific FE models, each specimen-specific FE model was reconfigured into three different morphologies using FE mesh morphing techniques. Significantly different strains were found comparing the gynecoid (classical female pelvis’) to the android (‘true male pelvis’) models (p = 0.040), with strains twice as high in the superior pubic rami. No significant differences were seen in comparing overall strains between the other pelvic shapes (p = 0.61–0.126). The highest strain regions in all models were found in the supra-acetabular regions, with high strains also found in the regions of the superior pubic rami, the greater sciatic notch and sacral regions about the L5 vertebrae. Quantifying the contributions of shape to strain in the pelvis may increase the understanding of sex and patient-specific differences in fracture risk and motivate the consideration of treatment strategies that account for anatomic pelvic differences.