Local Fiber Orientation Research Articles

Fiber Orientation Quantification for Large Area Additively Manufactured Parts Using SEM Imaging.

Polymer-based additively manufactured parts are increasing in popularity for industrial applications due to their ease of manufacturing and design form freedom, but their structural and thermal performances are often limited to those of the base polymer system. These limitations can be mitigated by the addition of carbon fiber reinforcements to the polymer matrix, which enhances both the structural performance and the dimensional stability during cooling. The local fiber orientation within the processed beads directly impacts the mechanical and thermal performances, and correlating the orientation to processing parameter variations would lead to better part quality. This study presents a novel approach for analyzing the spatially varying fiber orientation through the use of scanning electron microscopy (SEM). This paper presents the sample preparation procedure including SEM image acquisition and analysis methods to quantify the internal fiber orientation of additively manufactured carbon fiber-reinforced composites. Large area additively manufactured beads with 13% by weight large aspect ratio carbon fiber-reinforced acrylonitrile butadiene styrene (ABS) pellets are the feedstock used in this study. Fiber orientation is quantified using the method of ellipses (MoE), and the spatial change in fiber orientation across the deposited bead cross-section is studied as a function of process parameters including extrusion speed, raster height, and extrusion temperature zones. The results in the present paper show the results from the novel use of SEM to obtain the local fiber orientation, and results show the variation in alignment within the individual processed bead as well as an overall aligned orientation state along the direction of deposition.

Micromechanical modelling of short fibre composites considering fibre length distributions

Mechanical response of short fibre composites is varying locally with respect to the microstructural constitution of the material, which in turn is a consequence of flow conditions during manufacturing. This local constitution is described by local fibre volume content, local fibre orientation distribution and local fibre length distribution. For short fibre reinforced plastics, both distributions are affected by flow conditions during an injection moulding process. Current material models for predicting the homogenised material response account for the local volume fraction and local fibre orientation distribution. Fibre length distribution, however, is usually approximated with a single average fibre length. To investigate the effects of fibre length distribution on the elasto-plastic response of short fibre composites, a micromechanical Orientation Averaging model has been extended. Two methods are presented in this work. In the first method, an additional averaging scheme over the fibre length distribution is included. In the second method, a novel representative fibre length is presented based on a stiffness-weighted average. The predictionsobtained from these methods are then compared and evaluated against experimental results of uniaxial tensile tests taken from literature. Good agreements are found using both methods. However, for the investigated behaviour, using a representative fibre length is still beneficial due to the superior computational performance.

Spatially optimised fibre-reinforced composites with isosurface-controlled additive manufacturing constraints

A design approach accounting for manufacturing constraints is described for spatially optimised fibre-reinforced composites. The approach is based on the optimisation of local fibre orientation, the fibre volume fraction and density-based topology optimisation to determine the optimal design. A continuity equation is adopted to constrain the fibre orientation and ensure continuous fibres within the bounds of realistic fibre volume fractions. This results in a fibre orientation with a corresponding and controllable variation of the fibre volume fraction. In order to ensure the continuous fibre can be deposited, the manufacturability of the optimised results is ensured by introducing constraints controlled with two scalar fields to reconstruct fibre paths which are able to provide sufficient information to generate printer toolpaths. A cantilever beam problem is solved to show the advantage of the fibre reinforcement, the inclusion of manufacturing constraints and the penalty in compliance due to the application of the manufacturing constraints. The results show that the presented approach successfully guarantees the manufacturability with minimal loss of performance.

Non-Destructive Evaluation of In-Plane Waviness in Carbon Fiber Laminates Using Eddy Current Testing

Non-destructive detection of the in-plane waviness of carbon fiber-reinforced plastic (CFRP) laminates is of interest in a wide variety of industries, as wrinkles and other fiber alignment defects significantly impact the mechanical performance of the composites. This work demonstrates a method to detect in-plane wrinkles on a 5-ply unidirectional CFRP laminate with a customized eddy current testing (ECT) system. The results show that the ECT system is effective in detecting and quantifying in-plane waviness, and the results are compared to conventional X-ray computed tomography (CT) and ultrasonic testing (UT) methods. Using the anisotropic conductive nature of the aligned CFRP lamina, the ECT system was able to clearly detect throughout the part changes in the local fiber orientation, wave tangent angle, and wrinkle width.

Automatic segmentation and fibre orientation estimation from low resolution X-ray computed tomography images of 3D woven composites

In this work, we compare different methods for the automatic segmentation of microtomographic images at the mesoscopic scale of 3D woven carbon fibre composite materials. We use a dense neural network approach fed with features based on the monogenic signal and features learned by a convolutional neural network. To determine local fibre orientations in the composite, we also propose to use the monogenic signal with a structure tensor approach. The performance of the segmentation methods is compared on a first sample for which a reference solution is given. To demonstrate the global capability of the method, the neural networks are then used to segment a second similar sample, for which there is no reference solution. A transfer learning strategy is proposed to take advantage of the knowledge learned on the first sample and to limit the interaction with the user. This simply consists of re-learning only the decision layers, thus limiting the number of examples required. Finally, the best result is obtained with a combination of the signal-based approach and the learning-based approach by merging the monogenic and convolutional neural network features.

Numerical modelling on polymer composites prepared via large area extrusion-deposition additive manufacturing: fibre orientation, material inhomogeneity, and thermal-mechanical responses during material loading process

ABSTRACT The flow-induced fibre orientation formed during polymer extrusions causes the composite to exhibit non-homogeneous thermal-mechanical behaviours during Large Area extrusion-deposition Additive Manufacturing (LAAM) processes. This study numerically evaluates the fibre orientation state of a 20 wt.% short carbon fibre reinforced polyethylenimine fabricated by LAAM. The fibre orientation state of the solidified deposited bead is determined by a fully coupled flow/orientation simulation approach. The material properties of deposited composites are computed by assuming that the deposited bead has heterogeneous regions with varying local fibre orientation states. A finite element simulation is performed to model the LAAM process of a thin-wall structure, where the predicted inhomogeneous material properties are employed. Computed results show notable differences between simulations performed by employing homogenous properties and those obtained using heterogeneous properties. The bead-direction tensile stress contours computed under the heterogeneous assumption are comparable to experimental data in the literature, supporting our numerical approach.

Machine vision system for digital twin modeling of composite structures

Although the structural design of composite structures has already been carried out on a virtual level, composite mechanical properties remain sensitive to fiber orientation and therefore to the quality and reliability of the production process. Considering both manual single-unit manufacturing and advanced mass-unit fabrication, requirements on the production quality may differ, but certainty on the achieved result is crucial. A digital twin model, deterministically derived from produced parts, can be transferred into a virtual simulation environment to check for potential deviations of fiber alignment, resulting from variations in source material or composite production. Transferring that deterministic information into a virtual simulation environment allows for an estimation of the part’s structural potential despite any possible deviations by carrying out numerical simulation predictions on that model. This step of quality assessment can help reduce scrap parts by relying on simulation data that may demonstrate the feasibility of parts despite the containment of deviations with an otherwise uncertain impact. Therefore, further steps toward digitalization of the composite production process chain, especially on the characterization of the production quality, are aspired. In this contribution, a vision system based on a Microsoft Azure Kinect RGB-D camera is introduced which is used to digitalize the composite preform configuration from machine vision data by evaluating the achieved local fiber orientation as result of the complex preform draping process by digital image processing. A digital workflow is introduced that enables to feed the captured real-world data back into a digital environment where numerical simulations with the “as-built” fiber orientation can be carried out. The obtained results are used for assessing production quality and composite performance in the presence of possible deviations. The system, which consists of a camera array of consumer grade, can acquire real-world data and then transfer the data into a virtual environment.

A strain rate dependent and anisotropic failure material model for short fiber reinforced plastics based on energy density accounting for local fiber orientation

A failure model for SFRP for FEM simulations is developed to describe the strain rate dependency, the influence of the local fiber orientation and of the stress state on the failure behavior. The material is considered as a continuum while internally calculating the micro-mechanics analytically. The described micro-mechanics are based on experimental observations and on analyzation with numerical studies. In particular the strain rate dependent delamination of fibers and matrix is incorporated in the model. The distortion energy density is defined as the driving value for failure and estimated by the model. This is achieved with the analytic solution by Eshelby for the stress field in the matrix and by introducing an additional phase for the plasticly deformed volume. The validation on characterization specimens as well as component test demonstrates that the influence of strain rate, fiber orientation, and stress state on the failure behavior can be described with only one material parameter, the critical distortion energy density.

Modeling the effect of automated fiber placement induced gaps based on serial layer scanned point clouds

The gap is one of the most common defects during the AFP process, which has a nonnegligible impact on the mechanical properties of laminates. This paper proposes a novel three-dimension reconstructive gap model based on the in-process serial layer scanned point clouds, which can characterizes the gap features inside the laminate and pre-determine the mechanical performance before the subsequent procedures. The measured point cloud will be transformed into eligible and processible formats and then be used to generate a finite element grid using the element-based material property assignment method. The local material properties and fiber orientation are defined by previously assigned classes and grid geometries respectively. The effect of gap size on the in-plane tensile strength and damage evolution of laminates is investigated and both the experimental and simulation results find that the ultimate strength and stiffness show a significant decrease with the increase of gap size.

Data‐based prediction of the viscoelastic behavior of short fiber reinforced composites

AbstractThe viscoelastic behavior of short fiber reinforced polymers (SFRPs) partly depends on different microstructural parameters such as the local fiber orientation distribution. To account for this by simulation on component level, two‐scale methods couple simulations on the micro‐ and macroscale, which involve considerable computational costs. To circumvent this problem, the generation of a viscoelastic surrogate model is presented here. For that purpose, an adaptive sampling technique is investigated and data are obtained by creep simulations of representative volume elements (RVEs) using a fast Fourier transform (FFT) based homogenization method. Numerical tests confirm the high accuracy of the surrogate model. The possibility of using that model for efficient material optimization is shown.

Application of tailored fiber placement to fabricate automotive composite components with complex geometries

Fabrication of continuous carbon fiber composite components with complex geometries has traditionally been a challenge, yet automotive components are rarely simple in design or loading. Unidirectional and woven fabrics dominate the industry but are limited to generally homogenous microstructure across a layer. Tailored fiber placement (TFP) is a relatively new composite preforming technology that provides a means to address this limitation. In TFP, tows of fibers are positioned by a robotic gantry and stitched in place. This process affords unmatched control over the local fiber orientation. In this study, we design, fabricate, and test a simplified carbon fiber composite connecting rod for an internal combustion engine. Design is aided by performance simulation using the finite element BSAM program. The connecting rods are tested in monotonic compression, monotonic tension, and fatigue. Results show that the TFP process is able to successfully fabricate a high strength carbon fiber composite component featuring a complex structure. Discussion focuses on evaluating achieved performance relative to design targets, correlation between simulated predictions and test results, and evaluation of failure modes from post-failure analysis. The TFP process could be useful to many automotive applications, including seat frames, chassis components, and electric propulsion system components.

Performance of timber board models for prediction of local bending stiffness and strength - with application on Douglas fir sawn timber

Efficient utilization of structural timber requires accurate methods for machine strength grading. One of the most accurate methods presented this far is based on data of local fiber orientation on board surfaces, obtained from laser scanning. In this paper, two potential improvements of this method are examined. The first one consists of replacing a model based on simple integration over cross sections of boards for calculation of local bending stiffness by a 3D solid finite element (FE) model from which local bending stiffness is derived. The second improvement concerns replacement of a simple model for the fiber orientation in the interior of board by a more advanced one taking location of pith and growth direction of knots into account. Application of the alternative models on a sample of more than 200 Douglas fir boards, size 40 mm X 100 mm X 3000 mm, cut from large logs, show that each of the evaluated model improvements contributes to improved grading accuracy. When local bending stiffness is calculated utilizing the herein suggested FE model in combination with the improved model of fiber orientation in the interior of boards, a coefficient of determination to bending strength as high as 0.76 is obtained. For comparison, a coefficient of determination of 0.71 is obtain using the simpler original models.

Quality Analysis of Weld-Line Defects in Carbon Fibre Reinforced Sheet Moulding Compounds by Automated Eddy Current Scanning

Discontinuous fibre reinforced composites enable the manufacture of integrated structural components via the complex flow process of compression moulding. However, such processes can lead to the formation of detrimental weld-lines. Here, the meso-structure of carbon fibre sheet moulding compounds (C-SMC) was analysed using conventional non-destructive techniques and automated eddy current (EC) scanning, as well as destructive methods, in an attempt to identify defects such as weld-lines in this class of materials. Compression-moulded plaques with forced weld-lines in two different configurations (adjacent and opposing flow joints) were analysed, showing up to 80% strength reduction versus a defect-free plaque. The EC-determined local fibre orientation and elucidated local microstructure matched those obtained using conventional techniques, showing a dramatic fibre tow alignment parallel to the weld-lines. It was found that failure occurred in proximity to the “non-uniformity” defect regions identified by EC analyses, demonstrating the use of robot-guided EC for successful defect detection in C-SMC structures.

Rule-based definition of muscle bundles in patient-specific models of the left atrium.

Atrial fibrillation (AF) is the most common arrhythmia encountered clinically, and as the population ages, its prevalence is increasing. Although the CHA2DS2− VASc score is the most used risk-stratification system for stroke risk in AF, it lacks personalization. Patient-specific computer models of the atria can facilitate personalized risk assessment and treatment planning. However, a challenge faced in creating such models is the complexity of the atrial muscle arrangement and its influence on the atrial fiber architecture. This work proposes a semi-automated rule-based algorithm to generate the local fiber orientation in the left atrium (LA). We use the solutions of several harmonic equations to decompose the LA anatomy into subregions. Solution gradients define a two-layer fiber field in each subregion. The robustness of our approach is demonstrated by recreating the fiber orientation on nine models of the LA obtained from AF patients who underwent WATCHMAN device implantation. This cohort of patients encompasses a variety of morphology variants of the left atrium, both in terms of the left atrial appendages (LAAs) and the number of pulmonary veins (PVs). We test the fiber construction algorithm by performing electrophysiology (EP) simulations. Furthermore, this study is the first to compare its results with other rule-based algorithms for the LA fiber architecture definition available in the literature. This analysis suggests that a multi-layer fiber architecture is important to capture complex electrical activation patterns. A notable advantage of our approach is the ability to reconstruct the main LA fiber bundles in a variety of morphologies while solving for a small number of harmonic fields, leading to a comparatively straightforward and reproducible approach.

Fast data-driven computation and intuitive visualization of fiber orientation uncertainty in 3D-polarized light imaging

In recent years, the microscopy technology referred to as Polarized Light Imaging (3D-PLI) has successfully been established to study the brain’s nerve fiber architecture at the micrometer scale. The myelinated axons of the nervous tissue introduce optical birefringence that can be used to contrast nerve fibers and their tracts from each other. Beyond the generation of contrast, 3D-PLI renders the estimation of local fiber orientations possible. To do so, unstained histological brain sections of 70 μm thickness cut at a cryo-microtome were scanned in a polarimetric setup using rotating polarizing filter elements while keeping the sample unmoved. To address the fundamental question of brain connectivity, i. e., revealing the detailed organizational principles of the brain’s intricate neural networks, the tracing of fiber structures across volumes has to be performed at the microscale. This requires a sound basis for describing the in-plane and out-of-plane orientations of each potential fiber (axis) in each voxel, including information about the confidence level (uncertainty) of the orientation estimates. By this means, complex fiber constellations, e. g., at the white matter to gray matter transition zones or brain regions with low myelination (i. e., low birefringence signal), as can be found in the cerebral cortex, become quantifiable in a reliable manner. Unfortunately, this uncertainty information comes with the high computational price of their underlying Monte-Carlo sampling methods and the lack of a proper visualization. In the presented work, we propose a supervised machine learning approach to estimate the uncertainty of the inferred model parameters. It is shown that the parameter uncertainties strongly correlate with simple, physically explainable features derived from the signal strength. After fitting these correlations using a small sub-sample of the data, the uncertainties can be predicted for the remaining data set with high precision. This reduces the required computation time by more than two orders of magnitude. Additionally, a new visualization of the derived three-dimensional nerve fiber information, including the orientation uncertainty based on ellipsoids, is introduced. This technique makes the derived orientation uncertainty information visually interpretable.

Flow-induced fiber displacement in non-bindered UD-NCF during Wet Compression Molding – Analysis and implications for process control

Wet compression molding (WCM) provides high-volume production potential for continuously fiber-reinforced composite components with thermoset resins. Reduced cycle times are enabled by simultaneous infiltration, draping and consolidation within a single process step. During compression, the matrix is forced through the macroscopic stack and across its surface. This can cause undesired fiber displacements, which affect the structural performance of the processed part, as local fiber orientations can deviate from the intended one. Since already minor changes in fiber orientation can decrease the part performance, this process defect, often referred to as flow-induced fiber displacement (FiFD), should be avoided during processing. To prevent this, the WCM compression step is comprehensively evaluated by two experimental setups. First, systematic industrial-scale trials provide insight into the correlation between applied mold closing profile and resulting superficial FiFD on macro-scale. Second, a setup with transparent plates is applied to investigate the impact of superficial fluid at the stack surface on the obtained FiFD. Experimental results show positive correlations between velocity and viscosity of superficial fluid flow and observed FiFD. In this context, a more sophisticated closing amplitude of the press in combination with an adapted temperature control of the resin is proposed to minimize undesired fiber displacements.

Process-induced failure mode transition of compression molded discontinuous carbon fiber composites: From coupon to component level

Discontinuous carbon fiber composites have been reported to be damage tolerant materials with insensitivity to notches and defects. Meso-scale damage mechanisms and crack arresting are responsible for high apparent fracture toughness and pseudo-ductility. This work studies the effect of manufacturing and structure formation on the mechanical behavior of stochastic carbon fiber sheet molding compounds based on thermosetting prepreg platelets with a fiber length of 50 mm. The process–structure–property–performance relationship is analyzed by non-destructive characterization and mechanical bending tests on the component level. Model components of various preform and stack placement scenarios were compression molded to investigate different flow conditions, weld lines and artificially introduced defects. The experimental findings revealed a process-induced failure mode transition from pseudo-ductile to brittle failure behavior. Low-flow scenarios showed a highly non-linear response with large deflection values and high energy absorption. Higher in-mold material flow and weld lines caused brittle failure at significantly lower deflections with a considerable reduction of energy absorbing capacity. Increased material flow also reduced component stiffness and maximum load values. Weld lines exhibited major strength reduction compared to the pristine material configuration. Full-scale and high-resolution X-ray computed tomography scans revealed differences in local fiber orientation state, increased levels of fiber waviness and porosity, as well as asymmetric distribution of defects.

Deep Diffusion MRI Registration (DDMReg): A Deep Learning Method for Diffusion MRI Registration.

In this paper, we present a deep learning method, DDMReg, for accurate registration between diffusion MRI (dMRI) datasets. In dMRI registration, the goal is to spatially align brain anatomical structures while ensuring that local fiber orientations remain consistent with the underlying white matter fiber tract anatomy. DDMReg is a novel method that uses joint whole-brain and tract-specific information for dMRI registration. Based on the successful VoxelMorph framework for image registration, we propose a novel registration architecture that leverages not only whole brain information but also tract-specific fiber orientation information. DDMReg is an unsupervised method for deformable registration between pairs of dMRI datasets: it does not require nonlinearly pre-registered training data or the corresponding deformation fields as ground truth. We perform comparisons with four state-of-the-art registration methods on multiple independently acquired datasets from different populations (including teenagers, young and elderly adults) and different imaging protocols and scanners. We evaluate the registration performance by assessing the ability to align anatomically corresponding brain structures and ensure fiber spatial agreement between different subjects after registration. Experimental results show that DDMReg obtains significantly improved registration performance compared to the state-of-the-art methods. Importantly, we demonstrate successful generalization of DDMReg to dMRI data from different populations with varying ages and acquired using different acquisition protocols and different scanners.

Binocular Vision-Based Yarn Orientation Measurement of Biaxial Weft-Knitted Composites.

The mechanical properties of fiber-reinforced composites are highly dependent on the local fiber orientation. In this study, a low-cost yarn orientation reconstruction approach for the composite components’ surface was built, utilizing binocular structured light detection technology to accomplish the effective fiber orientation detection of composite surfaces. It enables the quick acquisition of samples of the revolving body shape without blind spots with an electric turntable. Four collecting operations may completely cover the sample surface, the trajectory recognition coverage rate reached 80%, and the manual verification of the yarn space deviation showed good agreement with the automated technique. The results demonstrated that the developed system based on the proposed method can achieve the automatic recognition of yarn paths of views with different angles, which mostly satisfied quality control criteria in actual manufacturing processes.

Association of Collagen, Elastin, Glycosaminoglycans, and Macrophages With Tissue Ultimate Material Strength and Stretch in Human Thoracic Aortic Aneurysms: A Uniaxial Tension Study.

Fiber structures and pathological features, e.g., inflammation and glycosaminoglycan (GAG) deposition, are the primary determinants of aortic mechanical properties which are associated with the development of an aneurysm. This study is designed to quantify the association of tissue ultimate strength and extensibility with the structural percentage of different components, in particular, GAG, and local fiber orientation. Thoracic aortic aneurysm (TAA) tissues from eight patients were collected. Ninety-six tissue strips of thickened intima, media, and adventitia were prepared for uni-extension tests and histopathological examination. Area ratios of collagen, elastin, macrophage and GAG, and collagen fiber dispersion were quantified. Collagen, elastin, and GAG were layer-dependent and the inflammatory burden in all layers was low. The local GAG ratio was negatively associated with the collagen ratio (r2 = 0.173, p < 0.05), but positively with elastin (r2 = 0.037, p < 0.05). Higher GAG deposition resulted in larger local collagen fiber dispersion in the media and adventitia, but not in the intima. The ultimate stretch in both axial and circumferential directions was exclusively associated with elastin ratio (axial: r2 = 0.186, p = 0.04; circumferential: r2 = 0.175, p = 0.04). Multivariate analysis showed that collagen and GAG contents were both associated with ultimate strength in the circumferential direction, but not with the axial direction (collagen: slope = 27.3, GAG: slope = -18.4, r2 = 0.438, p = 0.002). GAG may play important roles in TAA material strength. Their deposition was found to be associated positively with the local collagen fiber dispersion and negatively with ultimate strength in the circumferential direction.