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1. Deep 3D Convolution Neural Network Methods for Brain White Matter Hybrid Computational Simulations

   https://doi.org/10.1115/IMECE2020-24664  

   Wu, Xuehai; Pelegri, Assimina A. (November 2020, International Mechanical Engineering Congress and Exposition, IMECE 2020)

   Material properties of brain white matter (BWM) show high anisotropy due to the complicated internal three-dimensional microstructure and variant interaction between heterogeneous brain-tissue (axon, myelin, and glia). From our previous study, finite element methods were used to merge micro-scale Representative Volume Elements (RVE) with orthotropic frequency domain viscoelasticity to an integral macro-scale BWM. Quantification of the micro-scale RVE with anisotropic frequency domain viscoelasticity is the core challenge in this study. The RVE behavior is expressed by a viscoelastic constitutive material model, in which the frequency-related viscoelastic properties are imparted as storage modulus and loss modulus for the composite comprised of axonal fibers and extracellular glia. Using finite elements to build RVEs with anisotropic frequency domain viscoelastic material properties is computationally very consuming and resource-draining. Additionally, it is very challenging to build every single RVE using finite elements since the architecture of each RVE is arbitrary in an infinite data set. The architecture information encoded in the voxelized location is employed as input data and is consequently incorporated into a deep 3D convolution neural network (CNN) model that cross-references the RVEs’ material properties (output data). The output data (RVEs’ material properties) is calculated in parallel using an in-house developed finite element method, which models RVE samples of axon-myelin-glia composites. This novel combination of the CNN-RVE method achieved a dramatic reduction in the computation time compared with directly using finite element methods currently present in the literature. 

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2. Brain White Matter Model of Orthotropic Viscoelastic Properties in Frequency Domain

   https://doi.org/10.1115/IMECE2019-12182  

   Wu, Xuehai; Georgiadis, John G.; Pelegri, Assimina A. (November 2019, ASME INternational Mechanical Engineering Congress and Exposition)

   Finite element analysis is used to study brain axonal injury and develop Brain White Matter (BWM) models while accounting for both the strain magnitude and the strain rate. These models are becoming more sophisticated and complicated due to the complex nature of the BMW composite structure with different material properties for each constituent phase. State-of-the-art studies focus on employing techniques that combine information about the local axonal directionality in different areas of the brain with diagnostic tools such as Diffusion-Weighted Magnetic Resonance Imaging (Diffusion-MRI). The diffusion-MRI data offers localization and orientation information of axonal tracks which are analyzed in finite element models to simulate virtual loading scenarios. Here, a BMW biphasic material model comprised of axons and neuroglia is considered. The model’s architectural anisotropy represented by a multitude of axonal orientations, that depend on specific brain regions, adds to its complexity. During this effort, we develop a finite element method to merge micro-scale Representative Volume Elements (RVEs) with orthotropic frequency domain viscoelasticity to an integrated macro-scale BWM finite element model, which incorporates local axonal orientation. Previous studies of this group focused on building RVEs that combined different volume fractions of axons and neuroglia and simulating their anisotropic viscoelastic properties. Via the proposed model, we can assign material properties and local architecture on each element based on the information from the orientation of the axonal traces. Consecutively, a BWM finite element model is derived with fully defined both material properties and material orientation. The frequency-domain dynamic response of the BMW model is analyzed to simulate larger scale diagnostic modalities such as MRI and MRE. 

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3. Validation of a finite element method for simulation of components produced by continuous carbon fiber reinforced additive manufacturing

   https://doi.org/10.1080/15502287.2021.1946620  

   Mosfequr Rahman, J. Chandler (July 2021, International journal for computational methods in engineering science and mechanics)

   Taylor & Francis (Ed.)

   n this research, a method is examined by which the behavior of continuous carbon fiber rein-forced additive manufacturing may be simulated using Finite Element Analysis. This technique is used in a simulated tensile test experiment in which the findings are compared to results determined from theoretical calculations according to the Rule of Mixtures method and from existing mechanical testing results. Four different fiber reinforcement configurations are examined with fiber volume fractions ranging from 4% to 32%. It was found that for fiber vol-ume fractions of 11%, the simulation results closely match those predicted theoretically by the Rule of Mixtures as well as the mechanical testing results published in existing research. Lower fiber volume fractions near 4% yield less accurate results, with a 20% error due to the fact that the anisotropic behavior of the polymer matrix is the dominant material trait. Simulation of higher volume fractions near 32% closely approximate theoretical predictions, however neither the theoretical results nor the simulation results accurately reflect real world mechanical testing, indicating that nonideal condition factors such as the effect of micro-voids between the start and end of the fiber reinforcements play a significant role in the overall strength of the material. Thus, for fiber volume fractions near 11%, this simulation method can accurately be used to predict the behavior of end-use components, but more study must be done to increase simulation accuracy in low and high fiber volume fractions. 

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4. A Computational Framework for Predicting Properties From Multifield Processing Conditions in Polymer Matrix Composites

   https://doi.org/10.1115/SMASIS2020-2390  

   Widdowson, Denise; von Lockette, Paris; Erol, Anil; Rodriguez, Manuel A. (September 2020, ASME 2020 Conference on Smart Materials, Adaptive Structures and Intelligent Systems)

   null (Ed.)

   Abstract Composites can be tailored to specific applications by adjusting process variables. These variables include those related to composition, such as volume fraction of the constituents and those associated with processing methods, methods that can affect composite topology. In the case of particle matrix composites, orientation of the inclusions affects the resulting composite properties, particularly so in instances where the particles can be oriented and arranged into structures. In this work, we study the effects of coupled electric and magnetic field processing with externally applied fields on those structures, and consequently on the resulting material properties that arise. The ability to vary these processing conditions with the goal of generating microstructures that yield target material properties adds an additional level of control to the design of composite material properties. Moreover, while analytical models allow for the prediction of resulting composite properties from constituents and composite topology, these models do not build upward from process variables to make these predictions. This work couples simulation of the formation of microscale architectures, which result from coupled electric and magnetic field processing of particulate filled polymer matrix composites, with finite element analysis of those structures to provide a direct and explicit linkages between process, structure, and properties. This work demonstrates the utility of these method as a tool for determining composite properties from constituent and processing parameters. Initial particle dynamics simulation incorporating electromagnetic responses between particles and between the particles and the applied fields, including dielectrophoresis, are used to stochastically generate representative volume elements for a given set of process variables. Next, these RVEs are analyzed as periodic structures using FEA yielding bulk material properties. The results are shown to converge for simulation size and discretization, validating the RVE as an appropriate representation of the composite volume. Calculated material properties are compared to traditional effective medium theory models. Simulations allow for mapping of composite properties with respect to not only composition, but also fundamentally from processing simulations that yield varying particle configurations, a step not present in traditional or more modern effective medium theories such as the Halpin Tsai or double-inclusion theories. 

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5. Dual-zone material assignment method for correcting partial volume effects in image-based bone models

   https://doi.org/10.1080/10255842.2022.2119383  

   Inglis, Brendan; Grumbles, Daniel; Dailey, Hannah L. (September 2022, Computer Methods in Biomechanics and Biomedical Engineering)

   In image-based finite element analysis of bone, partial volume effects (PVEs) arise from image blur at tissue boundaries and as a byproduct of geometric reconstruction and meshing during model creation. In this study, we developed and validated a material assignment approach to mitigate partial volume effects. Our validation data consisted of physical torsion testing of intact tibiae from N = 20 Swiss alpine sheep. We created finite element models from micro-CT scans of these tibiae using three popular element types (10-node tetrahedral, 8-node hexahedral, and 20-node hexahedral). Without partial volume management, the models over-predicted the torsional rigidity compared to physical biomechanical tests. To address this problem, we implemented a dual-zone material model to treat elements that overlap low-density surface voxels as soft tissue rather than bone. After in situ inverse optimization, the dual-zone material model produced strong correlations and high absolute agreement between the virtual and physical tests. This suggests that with appropriate partial volume management, virtual mechanical testing can be a reliable surrogate for physical biomechanical testing. For maximum flexibility in partial volume management regardless of element type, we recommend the use of the following dual-zone material model for ovine tibiae: soft-tissue cutoff density of 665 mgHA/cm3 with a soft tissue modulus of 50 MPa (below cutoff) and a density-modulus conversion slope of 10,225 MPa-cm3/mgHA for bone (above cutoff). 

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