Local Fiber Orientation Research Articles

Tomographic modeling and internal structure analysis of engineering textiles: A parametric approach

Tomographic modeling of textiles is attractive for numerical investigation of composites due to its ability to reveal the internal architecture. However, reliability is notably dependent on geometrical modeling accuracy, which is still challenging. This paper addresses existing issues using a parametric approach that relies on statistical resampling and spatial autocorrelated prediction. The proposed strategy involves three stages: first, an explicit representation of each fiber tow is derived from the segmented images through statistical resampling in the parametric domain, which is subsequently mapped back to the physical domain according to known spatial autocorrelation. It allows depicting tow surfaces with varying levels of detail. Second, the tow trajectory is parameterized and subjected to a smoothing process, thereby identifying the local fiber orientation as its tangent vector. Finally, the normal cross-sections of tows are solved as intersecting implicit planes with a parametric tubular surface thanks to the parametric representation. This allows evaluating the spatial architectural variability in tows with notable waviness. Examinations were performed by contrasting the proposed approach with existing techniques. This new approach demonstrated better consistency with the ground truth while bringing additional benefits to the geometry reconstruction.

The use of digital thread for reconstruction of local fiber orientation in a compression molded pin bracket via deep learning

A deep convolutional neural network (DCNN) was used for microstructure reconstruction using artificial intelligence (MR-AI) by predicting local average fiber orientation distributions (FOD) in a 3D prepreg platelet molded composite (PPMC) pin bracket. To train the MR-AI model, surface strain fields from residual stresses simulated in PPMC plates were used as the input to the DCNN. A training dataset included PPMC plates with various degrees of global fiber alignment, based on the information obtained from high-fidelity flow simulation of a pin bracket. The MR-AI model was then deployed to analyze FOD in the 3D pin bracket by conducting thermo-elastic residual stress analysis. Initially, the MR-AI model was established entirely on the synthetic simulation data. Then, a μCT scan of a physically molded pin bracket was used to create a finite element model that provided data for additional validation of the DCNN model. For the μCT scan finite element pin bracket the MR-AI model predicted the distribution of fiber orientation tensor components with MAE of 0.10 indicating a global prediction error of 10 %. For the flow simulated pin bracket, the MR-AI model predicted the distribution of fiber orientation tensor components with a global prediction error of 11 %. The MR-AI model showed the ability to predict regions of varying alignment in the base and flange of the pin bracket. The proposed MR-AI methodology allows for rapid prediction of FOD in geometrically complex parts and offers a promising path to detecting unique fiber orientation states in molded components.

A Comparison of Skeletal Muscle Diffusion Tensor Imaging Tractography Seeding Methods.

The internal arrangement of a muscle's fibers with respect to its mechanical line of action (muscle architecture) is a major determinant of muscle function. Muscle architecture can be quantified using diffusion tensor magnetic resonance imaging-based tractography, which propagates streamlines from a set of seed points by integrating vectors that represent the direction of greatest water diffusion (and by inference, the local fiber orientation). Previous work has demonstrated that tractography outcomes are sensitive to the method for defining seed points, but this sensitivity has not been fully examined. To do so, we developed a realistic simulated muscle architecture and implemented four novel methods for tract seeding: seeding along the muscle-aponeurosis boundary with an updated procedure for rounding seed points prior to lookup in the muscle boundary mask and diffusion tensor matrix (APO-3); voxel-based seeding throughout the muscle volume at a user-specified spatial frequency (VXL-1); voxel-based seeding throughout the muscle volume at a variable spatial frequency (VXL-2), and seeding near external and internal muscle boundaries (VXL-3). We then implemented these methods in an example human dataset. The updated aponeurosis seeding procedures allow more accurate and robust tract propagation from seed points. The voxel-based seeding methods had quantification outcomes that closely matched the updated aponeurosis seeding method. Further, the voxel-based methods can accelerate the overall workflow and may be beneficial in high throughput analysis of multi-muscle datasets. Continued evaluation of these methods in a wider range of muscle architectures is warranted.

A laboratory method to determine 3D fibre orientation around knots in sawn timber: case study on a Douglas fir specimen

The mechanical properties of structural timber largely depend on the occurrence of knots and on fibre deviation in their vicinities. In recent strength grading machines, lasers and cameras are used to detect surface characteristics such as the size and position of knots and local fibre orientation. Since laser dot scanning only gives reliable information about the fibre orientation in the plane of board surfaces, simple assumptions are usually made to define the inner fibre orientation to model timber boards. Those models would be improved by better insight into real fibre deviation around knots. In the present work, a laboratory method is developed to evaluate growth layers geometries and fibre orientation, solely based on the fact that the fibers are parallel to the tree rings and without any further assumptions. The method simply relies on color scans and laser dot scans of Douglas fir (Pseudotsuga menziesii) timber specimen sections revealed by successive planing. The proposed method provides data on fibre orientation in 3D with an accuracy that is relevant for the calibration of detailed models.

Non-destructive evaluation of a composite honeycomb sandwich panel with in-plane waviness damage using ultrasonic guided waves

ABSTRACT Composite honeycomb sandwich structures (CHSS) are often manufactured using an autoclave co-cure process, which can introduce in-plane waviness (IW) damage due to fibre misalignment. Such fibre waviness, whether in-plane or out-of-plane, significantly weakens the strength of these structures. Therefore, developing reliable non-destructive evaluation (NDE) techniques is essential for detecting IW. This study presents an NDE technique utilising ultrasonic guided waves (GW) to identify IW damage in CHSS panels. A numerical model was created using COMSOL Multiphysics, where IW damage is simulated through curvilinear coordinate physics, followed by a time-dependent study of GW propagation. This model simulates local fibre orientation changes due to IW damage, enhancing our understanding of GW interaction with IW. Experimental investigations are performed using contact-type transducers to corroborate the numerical observations. The presence of in-plane waviness causes a reduction in the group velocity of the A0 mode, a characteristic that can be exploited for detecting in-plane fibre waviness in CHSS. Also, parametric studies were performed to examine the effects of IW severity (aspect ratio) and increased IW area on GW characteristics. Finally, a technique for IW damage localisation is demonstrated using a convex hull algorithm based on changes in correlation coefficient values of the A0 mode.

Recognition of local fiber orientation state in prepreg platelet molded composites via deep learning

This paper presents a novel deep learning approach for reconstruction of local through-the-thickness fiber orientation distribution (FOD) in discontinuous long-fiber, prepreg-platelet molded composites (PPMC). The thermal-residual strains on composite surfaces are used as inputs to train a fully convolutional neural network, named U-Net, to predict spatially varying, local FOD. High fidelity synthetic data was generated via computational simulation of PPMC with stochastic material orientation state and was used for training the deep learning model. The proposed U-Net model allowed for rapid recognition of PPMC morphology by solving the inverse structural mechanics problem of determining the average fiber orientation through the composite thickness based on the provided surface strain measurements. Upon training and validation, the U-Net deep learning model was deployed to rapidly predict complex distributions of the local through-the-thickness FOD in PPMC.

Buckling optimization of variable-stiffness composite plates with two circular holes using discrete Ritz method and potential flow

Potential flow around two equal-radius cylinders is derived analytically and applied to generate the curvilinear fiber path of variable-stiffness composite (VSC) plates with two circular holes. As complex variable theory and conformal mapping are used to generate the potential flow around two equal-radius cylinders, the location and size of the two equal-radius circular holes are arbitrary. By changing the angle of incoming flow, the global fiber angle of a variable-stiffness lamina can be simulated and the local fiber orientation angle at any point is determined by the global potential flow field. Buckling performance of variously-shaped VSC plates with two circular holes are investigated via a novel numerical method − discrete Ritz method (DRM). DRM combines the extended interval integral, Gauss quadrature, and variable stiffness within a rectangular domain and builds a discrete energy system to simulate the plate, which allows the geometric boundaries of the plate to vary. The strain energy of a plate is modeled in the rectangular domain, discretized using Gauss points, and is characterized by variable stiffness, which characterizes both the material distribution and plate geometry simultaneously. After that, a three-dimensional sampling optimization (3DSO) method is adopted to optimize the curvilinear fiber configurations of VSC plates, and its buckling performances are compared with those of constant stiffness composites (CSC) with optimal straight fibers. Significant improvements on load-carrying capacity can be achieved compared to straight ones, demonstrating that using potential flow is one of the most efficient way to generate curvilinear optimal fiber path with maximum load-carrying capacity for VSC plates with material discontinuity. Moreover, DRM exhibits good precision and stability for buckling analysis of VSC plates with complex geometries.

Automatic reconstruction of closely packed fabric composite RVEs using yarn-level micro-CT images processed by convolutional neural networks (CNNs) and based on physical characteristics

Micro-CT scanning is an advanced technique to reconstruct inner architectures for RVEs of fabric composites. Currently, however, there exist few automatic approaches to separate closely packed yarns in the micro-CT images with economical resolution that can only identify yarns but not individual fibers. To tackle this issue, an innovative method has been developed in this work to identify cross-section area of the yarns via deep-learning-based image segmentation, and then reconstruct 3D geometry model of the composites containing local fiber orientations. The image segmentation was achieved through semantic segmentation by U-Net with validation accuracy of 87 % counted by mIOU and object detection by YOLO v8 with validation accuracy of 99.5 % counted by mAP50. Micro-CT slices with different morphological characteristics were divided into three groups via a ResNet50-based image classification network and selected in ratio to form the training datasets for U-Net and YOLO v8 with high representativity and efficiency. Extraction of individual cross-sections of weft and warp yarns were conducted only using the micro-CT scanning from one angle of view to reduce scanning cost and yarn-to-yarn penetration error. An algorithm considering physical constraints of the yarns was also completed to estimate local fiber orientations with maximum error of 18.065°, nearly 50 % smaller than the existing method.

Predicting the fiber orientation of injection molded components and the geometry influence with neural networks

The injection molding simulation of short fiber reinforced plastics (SFRP) is time consuming. However, until now it is necessary for predicting the local fiber orientation, to optimize the molding process and to predict the mechanical behavior of the material. This research presents the capabilities of artificial neural networks (NN) in predicting fiber orientation tensor (FOT) during injection molding processes, with a focus on enhancing computational efficiency compared to traditional simulation methods. Three NN architectures are compared based on simulated injection molded plates, with the goal of predicting the effect of the plate geometry on the local fiber orientation. Results indicate that NN outperform the baseline assumption of aligned fibers and demonstrate significant potential for accurate FOT prediction. The computational efficiency of NN, especially during the prediction phase, showcases a reduction in processing time by a factor of 104 compared to traditional simulation methods. This research lays a foundation for further exploration into the feasibility of NN in partly replacing time-consuming simulations for practical applications in injection molding processes.

Analysis of the Influence of Fiber Orientations in Carbon Fiber Reinforced Composites on their Structural Properties Based on Eddy Current Measurements

Fiber-reinforced plastics (FRP) are a type of composite material consisting of a reinforcing structure and a plastic matrix. When compared to traditional construction materials, FRP has higher strength and stiffness due to the high mechanical properties of reinforcing fibers such as carbon or glass. However, the properties of FRP are dependent on the alignment of fibers within the composite, with deviations leading to reduced strength and stiffness. Eddy current testing is a non-destructive technique used to visualize carbon fibers in the composite and assess the impact of local fiber orientation on the structural properties of FRP. This study aims to understand the influence of local fiber orientation on tensile strength and elastic modulus by producing composites with defined fiber orientations, analyzing them with eddy current testing, and assessing their mechanical properties through tensile tests. The measured fiber orientations are then used to validate a finite element model, in which the actual, measured fiber orientation is applied to the simulation and correlated with the mechanical properties. In contrast to previous published studies measured fiber orientation is used, which as shown in this work, differs from the theoretically implemented fiber orientation.

Influence of fiber orientation on the high cycle tensile fatigue resistance of ultra-high performance fiber reinforced cementitious composites (UHPFRC)

This paper quantitatively studies the influence of fiber orientation on the high cycle tensile fatigue resistance of ultra-high performance fiber reinforced cementitious composites (UHPFRC) within and beyond elastic domain. Eight tensile fatigue tests and re-analysis of sixteen fatigue tests from a previous study are conducted, which have specimens statically preloaded to the strain from 0.19‰ to 0.5‰ and from 1.1‰ to 2‰, considered to be in the elastic and post-elastic domains in tension, respectively. For the eight fatigue tests, the local fiber orientation and dosage of each specimen are determined before testing using a magnetic probe. During testing, specimen behavior is characterized by displacement transducers, digital image correlation and acoustic emission. Based on the above, the fatigue endurance limit at 10 million cycles is determined to be about SUte,10%= 0.9 of the elastic limit tensile stress for UHPFRC within elastic domain. For UHPFRC preloaded beyond the elastic domain, the fatigue resistance decreases with increasing pre-applied strain and decreasing average fiber orientation (increasing average angle between fibers and principle tensile direction). With pre-applied strain from 1.1‰ to 2.0‰, the endurance limit may be taken as SUte,10%=0.60 to 0.65 for typical UHPFRC structures. Two original fatigue resistance models are proposed, which describe the relationship between average fiber orientation, number of cycles and maximum fatigue stress for UHPFRC within and beyond the elastic domain.

Fibre orientation distribution function mapping for short fibre polymer composite components from low resolution/large volume X-ray computed tomography

Short glass fibre injection moulded composites, used in interior and exterior automotive parts, are exposed to complex stress states, for example during a crash. As the fibre scale dominates the composite’s material properties, numerical models need to account for the local fibre orientation. In recent years, mould flow simulation results have been exploited to predict the fibre orientations for finite element models, albeit with limited accuracy. Alternatively, X-ray computed tomography can be used to directly image and analyse fibre orientations. Traditionally, achieving the necessary resolution to image individual fibres restricts the imaging to small regions of the component. However, this study takes advantage of recent advancements in imaging and image analysis to overcome this limitation. As a result, it introduces, for the first time, a reliable, fast, and automated fibre orientation mapping for a full component based on image analysis at the individual fibre level; even for cases where the pixel size is significantly larger than the fibre diameter. By scanning at lower resolutions, a drastically larger volume of interest can be achieved. The resulting fibre orientation analysis and mapping algorithm, based on X-ray computed tomography, is well matched to the level of information required for automotive crash modelling with a standard element-size of a few millimetres. The entire process, encompassing image acquisition, image analysis and fibre orientation mapping, can be directly integrated into an industrial full component application in a matter of hours.

In situ experimental investigation of fiber orientation kinetics during uniaxial extensional flow of polymer composites

The demand for fiber-filled polymers has witnessed a significant upswing in recent years. A comprehensive understanding of the local fiber orientation is imperative to accurately predict the mechanical properties of fiber-filled products. In this study, we experimentally investigated the fiber orientation kinetics in uniaxial extensional flows. For this, we equipped a rheometer with a Sentmanat extensional measurement device and with an optical train that allows us to measure the fiber orientation in situ during uniaxial extension using small angle light scattering. We investigated an experimental system with glass fibers for the suspended phase (L/D=8−15), and for the matrix either low density polyethylene, which shows strain hardening in extension, or linear low density polyethylene, which shows no strain hardening. For these two polymer matrices, the fiber orientation kinetics were investigated as a function of fiber volume fraction (ϕ=1%, 5%, and 10%) and Weissenberg number (by varying the Hencky strain rate, ϵ˙H=0.01−1s−1). We found that all these parameters did not influence the fiber orientation kinetics in uniaxial extension and that these kinetics can be described by a multiparticle model, based on Jeffery’s equation for single particles. Our results show that, in uniaxial extension, fiber orientation is solely determined by the applied strain and that, up to the concentrated regime (ϕ≈D/L), fiber-fiber interactions do not influence the fiber orientation. The extensional stress growth coefficient of these composites, which is measured simultaneously with the orientation, shows high agreement with Batchelor’s equation for rodlike suspensions.

Effect of Resin Bleed Out on Compaction Behavior of the Fiber Tow Gap Region during Automated Fiber Placement Manufacturing.

Automated fiber placement is a state-of-the-art manufacturing method which allows for precise control over layup design. However, AFP results in irregular morphology due to fiber tow deposition induced features such as tow gaps and overlaps. Factors such as the squeeze flow and resin bleed out, combined with large non-linear deformation, lead to morphological variability. To understand these complex interacting phenomena, a coupled multiphysics finite element framework was developed to simulate the compaction behavior around fiber tow gap regions, which consists of coupled chemo-rheological and flow-compaction analysis. The compaction analysis incorporated a visco-hyperelastic constitutive model with anisotropic tensorial prepreg viscosity, which depends on the resin degree of cure and local fiber orientation and volume fraction. The proposed methodology was validated using the compaction of unidirectional tows and layup with a fiber tow gap. The proposed approach considered the effect of resin bleed out into the gap region, leading to the formation of a resin-rich pocket with a complex non-uniform morphology.

Fibre and failure characterization in long glass fibre reinforced polypropylene by X-ray computed tomography

Long fibre reinforced thermoplastics (LFTs) have become important in recent years, due to outstanding mechanical properties (such as high strength, high stiffness, and excellent impact behaviour). Nevertheless, there is still a lack of knowledge regarding the failure micromechanics of LFTs. This publication focuses on the qualitative and quantitative analysis of influencing factors for failure initiation in long fibre reinforced thermoplastics. Existing and new methodologies are combined for detailed analysis of the local microstructure and micromechanical behaviour. In situ investigations of the LFT via X-ray computed tomography (CT) and subsequent fibre and mechanical characterization based on the volume data were performed. A measurement workflow for the decision on the scanning area before starting a scan series is suggested. The difficulties in fibre orientation analysis of long, curved fibres are revealed by a comparison of different evaluation approaches. Fibre straightness, fibre volume fraction, fibre orientation and fibre end accumulations are investigated in detail. Local strains are determined with digital volume correlation (DVC) and related to the true stress along the measured specimen length. The investigations showed that the test specimens do not necessarily break at the position of the smallest cross-section under uniaxial loading conditions. The failure behaviour of LFTs is strongly influenced by the local microstructure and thus the combination of various microstructural factors such as fibre bundles, fibre ends, local fibre volume fraction and orientation.

Experimental Validation of Reconstructed Microstructure via Deep Learning in Discontinuous Fiber Platelet Composite

Abstract A novel approach for microstructure reconstruction using artificial intelligence (MR-AI) was proposed to nondestructively measure the through-thickness average stochastic fiber orientation distribution (FOD) in a prepreg platelet molded composite (PPMC) plate. MR-AI approach uses thermal strain components on the surfaces of a PPMC plate as input to the deep learning model, which allows to predict a distribution of local through-thickness average fiber orientation state in the entire PPMC volume. The experimental setup with a heating stage and digital image correlation (DIC) was used to measure thermal strains on the surface of the PPMC plate. Optical microscopy was then used to measure FOD in the cross section of the PPMC plate. FOD measurements from optical microscopy imagery compared favorably with FOD prediction by MR-AI. The proposed methodology opens the opportunity for rapid, nondestructive inspection of manufacturing-induced FOD in molded composites.

Fiber alignment in 3D collagen networks as a biophysical marker for cell contractility

Cells cultured in 3D fibrous biopolymer matrices exert traction forces on their environment that induce deformations and remodeling of the fiber network. By measuring these deformations, the traction forces can be reconstructed if the mechanical properties of the matrix and the force-free matrix configuration are known. These requirements limit the applicability of traction force reconstruction in practice. In this study, we test whether force-induced matrix remodeling can instead be used as a proxy for cellular traction forces. We measure the traction forces of hepatic stellate cells and different glioblastoma cell lines and quantify matrix remodeling by measuring the fiber orientation and fiber density around these cells. In agreement with simulated fiber networks, we demonstrate that changes in local fiber orientation and density are directly related to cell forces. By resolving Rho-kinase (ROCK) inhibitor-induced changes of traction forces, fiber alignment, and fiber density in hepatic stellate cells, we show that the method is suitable for drug screening assays. We conclude that differences in local fiber orientation and density, which are easily measurable, can be used as a qualitative proxy for changes in traction forces. The method is available as an open-source Python package with a graphical user interface.

Biaxial stretch can overcome discrepancy between global and local orientations of wavy collagen fibres

Most frequently used structure-based constitutive models of arterial wall apply assumptions on two symmetric helical (and dispersed) fibre families which, however, are not well supported with histological findings where two collagen fibre families are seldom found. Moreover, bimodal distributions of fibre directions may originate also from their waviness combined with ignoring differences between local and global fibre orientations. In contrast, if the model parameters are identified without histological information on collagen fibre directions, the resulting mean angles of both fibre families are close to ±45°, which contradicts nearly all histologic findings. The presented study exploited automated polarized light microscopy for detection of collagen fibre directions in porcine aorta under different biaxial extensions and approximated the resulting histograms with unimodal and bimodal von Mises distributions. Their comparison showed dominantly circumferential orientation of collagen fibres. Their concentration parameter for unimodal distributions increased with circumferential load, no matter if acting uniaxially or equibiaxially. For bimodal distributions, the angle between both dominant fibre directions (chosen as measure of fibre alignment) decreased similarly for both uniaxial and equibiaxial loads. These results indicate the existence of a single family of wavy circumferential collagen fibres in all layers of the aortic wall. Bimodal distributions of fibre directions presented sometimes in literature may come rather from waviness of circumferentially arranged fibres than from two symmetric families of helical fibres. To obtain a final evidence, the fibre orientation should be analysed together with their waviness.

A topology-based in-plane filtering technique for the combined topology and discrete fiber orientation optimization

This work proposes a filtering technique for the concurrent and sequential finite element-based topology and discrete fiber orientation optimization of composite structures. The proposed filter is designed to couple the morphology with the topology of the structural domain throughout the optimization process in a way such that it suppresses the impact of the close-to-void finite elements’ morphology on the overall morphology of the structure while steering at the same time the local fiber orientation towards the neighboring topologically dense areas of the geometry. The functionality of the filter becomes crucial at the boundaries of the optimized structure, where the fiber orientation is forced to conform to the morphology of the local boundaries. The developed filtering technique is incorporated into both the concurrent and sequential finite element-based topology and discrete fiber orientation optimization problem, and the respective optimization problems are formulated for the compliance minimization of the composite structure. To assess the efficacy of the filter, it is demonstrated in the benchmark academic case studies of the 2D Messerschmitt-Bölkow-Blohm and cantilever beams when different state-of-art interpolation techniques are employed for modeling the discrete fiber orientation optimization problem.

Assessing thermophysical properties of parameterized woven composite models using image-based simulations

Mesoscale simulations of woven composites using parameterized analytical geometries offer a way to connect constituent material properties and their geometric arrangement to effective composite properties and performance. However, the reality of as-manufactured materials often differs from the ideal, both in terms of tow geometry and manufacturing heterogeneity. As such, resultant composite properties may differ from analytical predictions and exhibit significant local variations within a material.We employ mesoscale finite element method simulations to compare idealized analytical and as-manufactured woven composite materials and study the sensitivity of their effective properties to the mesoscale geometry. Three-dimensional geometries are reconstructed from X-ray computed tomography, image segmentation is performed using deep learning methods, and local fiber orientation is obtained using the structure tensor calculated from image scans. Suitable approximations to composite properties, using analytical unit cell calculations and effective media theory, are assessed. Our findings show that an analytical geometry and sub-unit cell geometry provide reasonable approximations of the full-scale predictions for the effective thermal properties of a multi-layer production composite.