3D Composites Research Articles

Probabilistic Source Classification of Large Tephra Producing Eruptions Using Supervised Machine Learning: An Example From the Alaska‐Aleutian Arc

AbstractAlaska contains over 130 volcanoes and volcanic fields that have been active within the last 2 million years. Of these, roughly 90 have erupted during the Holocene, with many characterized by at least one large explosive eruption. These large tephra‐producing eruptions (LTPEs) generate orders of magnitude more erupted material than a “typical” arc explosive eruption and distribute ash thousands of kilometers from their source. Because LTPEs occur infrequently, and the proximal explosive deposit record in Alaska is generally limited to the Holocene, we require a method that links distal deposits to a source volcano where the correlative proximal deposits from that eruption are no longer preserved. We present a model that accurately and confidently identifies LTPE volcanic sources in the Alaska‐Aleutian arc using only in situ geochemistry. The model is a voting ensemble classifier comprised of six conceptually different machine learning algorithms trained on proximal tephra deposits that have had their source positively identified. We show that incompatible trace element ratios (e.g., Nb/U, Th/La, Rb/Sm) help produce a feature space that contains significantly more variance than one produced by major element concentrations, ultimately creating a model that can achieve high accuracy, precision, and recall on predicted volcanic sources, regardless of the perceived 2D data distribution (i.e., bimodal, uniform, normal) or composition (i.e., andesite, trachyte, rhyolite) of that source. Finally, we apply our model to unidentified distal marine tephra deposits in the region to better understand explosive volcanism in the Alaska‐Aleutian arc, specifically its pre‐Holocene spatiotemporal distribution.

Alignment of 3D woven textile composites towards their ideal configurations

The objective of the present study is to create a parametric model of an ideal 3D woven textile from a computed tomography at mesoscale without prior knowledge of the fabric architecture. The model is constructed by identifying a minimal number of parameters from the tomography and includes further assumptions about the textile properties (e.g., equally-spaced vertical yarn columns). A novel registration procedure called Model-based Digital Image Correlation (MDIC) is introduced for mapping the whole textile image onto its own model. It leads to a realignment of the yarn columns after deforming the textile image, from which the model is updated. Model extraction and registration steps are iterated up to a stationary solution. The final result is a perfect textile geometry with straight and orthogonal yarn columns and its mapping onto the original tomography image. The proposed procedure is applied successfully to a 3D woven textile and a 3D-injected woven composite. This novel technique is useful as a pre-processing step to image segmentation procedures or to ease the visual inspection performed by operators in correcting the yarn paths and yarn column deformations occurring during composite material manufacturing. Additionally, this alignment procedure could be used to deform a numerical ideal model to better fit the geometry of a real weave.

Prediction of elastic properties of 3D4D rotary braided composites with voids using multi-scale finite element and surrogate models

The conventional evaluation of 3D braided composites' mechanical properties through numerical and experimental methodologies serves as a hindrance to material application owing to the considerable expenses, time constraints, and laborious efforts involved. Moreover, the presence of void defects induced during the processing exacerbates this challenge. In this study, a multi-scale finite element model (FEM) and a surrogate model are established for predicting elastic properties of three dimensional four directional (3D4D) rotary braided composites with voids for the first time. Based on the established FEM, a comprehensive dataset containing 768 data points is formed, covering the ranges of both design parameters and void defect parameters. The influence of braiding angle, yarn width, and porosity, on the elastic constants of 3D4D rotary braided composites is accurately analyzed. A genetic algorithm-optimized back propagation neural network (GABPNN) model is developed, which possess the capability to replicate FEM outcomes with a commendable R-value of 0.99. The remarkable concordance between the anticipated outcomes and experimental datasets corroborates the triumphant implementation of the present method in unraveling the interconnections between microstructure and properties in 3D4D rotary braided composites containing voids. Consequently, this offers a propitious instrument for expediting the intelligent conception and refinement of composite materials.

Fabrication of SnO2 micron-rods embedding in 3D composite of graphene oxide@MWNTs as high-performance anode for lithium-ion batteries

Tin dioxide-based anode materials have attracted extensive attention in lithium-ion batteries due to their high specific capacity. However, the low intrinsic conductivity and volume expansion of commercial SnO2, as well as the aggregation due to snation result in electrodes that are highly susceptible to crush during the cycling process. Here, SnO2@GO@MWNTs complex consisting of rod-like SnO2 with micron-size uniformly embedded in the multi-walled carbon nanotubes (MWNTs) and multilayer graphene oxide (GO) was constructed via a simple ultrasonic mixing method, and was employed as the anode materials of lithium-ion batteries. This composite electrode showed excellent cycling performance, exhibiting a high reversible capacity of 1242.6 mAh·g−1 after 280 cycles (at 200 mA·g−1), and excellent rate performance at current densities of 1000 mA·g−1 and 2000 mA·g−1, with high specific capacity of 770.5 mAh·g−1 and 640.1 mAh·g−1, respectively. The excellent electrochemical performance was attributed to the fact that the framework structure formed by GO and MWNTs effectively mitigates the volume expansion of rod-shaped SnO2 while improving its electrical conductivity, and the micron-sized rods shape alleviates the aggregation of particles due to the partial tinisation of SnO2.

Identification of the effective interface between the homogenized phases of a dual-weave 3D composite

Hybridization enhances composite properties by assembling several reinforcement materials. In woven composites, the distribution of different yarns can induce a macroscopic heterogeneity in the thermomechanical properties, which can be considered in the modeling through the introduction of homogenized phases regionally. Identifying the interfaces between those effective phases based on the geometry is difficult due to the yarn entanglement at the interfaces. This work proposes to identify the optimal positions of interfaces by observing the global mechanical behavior of the composite. An asymmetric glass/carbon-hybrid 3D-interlock fabric is studied. It results in a dual-weave composite modeled as macroscopically bi-phased material. It is shown that the effective interface position can be determined from a heating-cooling experiment tomographied in-situ, and processed by Integrated Digital Volume Correlation (IDVC), together with some of the thermomechanical properties of each phase. To enhance IDVC sensitivity, a specific procedure is designed to include the specimen boundary in the analysis.

Influence of realistic microscopic fiber misalignments on compressive damage mechanisms of 3D angle-interlock woven composites: In-situ measurements and numerical simulations

Stochastic fiber misalignments have been proven to have a significant impact on the compressive strength of traditional unidirectional laminates. However, these inherent manufacturing defects are rarely considered when dealing with 3D woven composites, due to the difficulty in characterizing the misalignment angles of individual fibers within all yarns. The current work provides the first in-situ measurements of fiber misalignments in 3D angle-interlock woven composites (3DAWCs) at the microscale, where the continuous fibers inside the warp, weft, and binder yarns are systematically examined using a high-resolution optical microscope. Statistical analysis reveals that the microscopic fiber misalignments follow a normal distribution, and the magnitude of the misalignment angle increases slightly with the degree of yarn fluctuation. Through a developed user subroutine, SDVINI, the obtained misalignment distributions are initialized in the corresponding yarns that belong to the constructed high-fidelity model. In comparison to the idealized one, the model introducing realistic fiber misalignments can effectively correct the overestimated predictions, and exhibits better agreement with the performed experiments. Furthermore, the influence of fiber misalignments on compressive damage mechanisms, particularly kinking failure, is also parametrically studied by incorporating different misalignment distributions.

Composite Forming Technology for Braiding Grid-Enhanced Structures and Design of a New Weaving Mechanism

Three-dimensional weaving structures have high strength and resistance to interlayer shear due to their integrated manufacturing features. The traditional weaving equipment is generally huge and is not effective for weaving small-sized products. Therefore, this paper proposes a new composite forming technology and weaving mechanism based on grid-enhanced structures to braid small-sized preforms, combining the continuous fiber printing technology, the rope drive technology and the 3D weaving technology. This paper adopted the screw theory for forward and inverse kinematic analysis of the weaving mechanism and applied software Adams2020 for trajectory simulation analysis. The theoretical calculation results were basically consistent with the simulation results, which verified the rationality and feasibility of the designed weaving mechanism in small, enhanced grids.

Advanced structural and multi‐functional sandwich composites with prismatic and foam cores: A review

AbstractAdvanced sandwich structures have emerged as new multifunctional structures with low density and good mechanical performance. Sandwich composites have high strength to weight and high stiffness to weight ratio. Good thermal and sound insulation can be provided using sandwich composite materials. Sandwich composites have high energy absorbing capacity. Multifunctional sandwich composites exhibit mechanical performance and other functional properties such as energy storage, electrical conductivity, etc. Further, the core can be used for routing wires, foam filling, fluid storage, embedding bars, etc. Thus, the multifunctionality can be introduced to the resuting composite materials. This article reviews current research on innovative sandwich structures with integrally woven corrugated core, honeycomb, foam, and 3D printed core architectures, emphasizing their multifunctionality. Aspects of sandwich structure with types of face and core structure design, material design, mechanical properties, and panel performance and damage, including multifunctional benefits, are reviewed. Examples of engineering and modern uses of sandwich structures are presented, covering hypersonic vehicles, civil and marine engineering, electronics, and biological fields. A list of areas for future study initiatives is also included. This review will serve as a baseline for future studies to do more research in the same direction.Highlights A detailed review of various types of core materials and sandwich structures is presented. Sandwich structures have high energy absorbing capacity. 3D woven sandwich structures, their manufacturing and failure mechanics is discussed. Modern applications of sandwich structures are detailed.

Electromechanical behavior and damage index system of 3D carbon fiber angle-interlock woven composites with FEA and data processing tools

The relationship between structural damage and electrical resistance change of carbon fiber reinforced composites is important to structural health monitoring. Here, we investigated the electromechanical behavior and damage index system for identification of various damage types in 3D angle-interlock woven composites under tensile loading in the warp direction. The mechanism of electrical resistance change was explored by combining current injected from both ends (Electrical current is in the plane of the laminate and goes through the entire cross section of the specimen) and current injected diagonally (Direction of electrical current between the in-plane direction and the through-thickness direction). A finite element model was established to analyze the electric potential distribution and tensile damage evolution process of composites. We found current injected from both ends can only detect with yarn damage and current injected diagonally can detect with matrix cracks, interface cracking and yarn damage. Based on the electrical resistance data of current injected diagonally, we used principal component analysis and K-means clustering methods to establish a damage index system to reflect the damage accumulation degree of different damage modes. The finite element analyses verified the rationality of the damage index system.

A simulation approach to auxetic and non-auxetic behavior of 3D composites produced with multi-cell flat-knitted spacer fabrics

The paper aims to simulate the auxetic and non-auxetic behavior of 3D composites reinforced with multi-cell flat-knitted spacer fabrics. Spacer weft knitted preforms were fabricated on an electronic flat knitting machine. 3D knitted composite samples with re-entrant, regular hexagonal and spear-head geometries, were prepared via vacuumed assisted resin transfer molding method. Using a cross-over geometrical model surrounded by a rigid resin cube, the composite unit-cell was designed in Abaqus software’s environment. Chamis micromechanical model was then used to determine the elastic constants of composite unit-cell. Meso-macro finite element analysis was used to simulate the response of the 3D composites to compressive loading. The results revealed that the Poisson’s ratio of re-entrant and hexagonal 3D knitted composite varied between −6 and −1 and 1.6 to 3.8, respectively. Also, the Poisson’s ratio of spear-head knitted composite was measured as zero. The proposed model was used to verify the predicted compressive behavior and Poisson’s ratio of the prepared composite samples. For hexagonal knitted composite, the proposed model demonstrated a good agreement with the experimental results. The Poisson’s ratio-strain curve obtained for 3D re-entrant knitted composite is highly compatible to those due to modelling results in strain range of about 5.8 to 22.5%. Also, for 3D spear-head knitted composite, the modelling results are not compatible to experimental method at strain values over 6%.

A compact yet flexible design space for large-scale nonperiodic 3D woven composites based on a weighted game for generating candidate tow architectures

Three-dimensional non-periodic woven composite preforms have sufficient design flexibility that tows can be aligned along principal loading paths even in shaped structural components with detailed local features. While this promises competitive performance, the feasible design space is combinatorially large, far beyond exhaustive search. Seeking a design space that is compact and easily searched yet can span the full potential of 3D weaving, we propose a method for generating candidate designs called the Background Vector Method (BVM) which treats weaving tows as agents in a game competing to match background vectors derived from different design requirements. The BVM generates candidate designs that adapt local architecture to global design goals by adjusting scalar weights. A manufacturing-based parameterization assures fabricability. The scope of possible designs and the speed of the BVM are illustrated by re-creating common periodic 3D weaving patterns and novel complex non-periodic architectures, with a route demonstrated to forming cavities, ducts, and other open volumes. How the BVM might be incorporated within an optimization algorithm is outlined and pathways are shown for systematically enlarging the design space as individual design problems may require.

Geometrical modeling and experimental validation of 3D woven honeycomb fabric for lightweight aircrew helmet liner manufacturing

This research focused on the development of a honeycomb fabric by predicting the areal density suitable for aircrew helmet liner. The aim was to fulfill the requirements for the lightweight and mechanical properties of composite liner. The model was based on the structural characteristics of the preform, including the weave design and geometric parameters. The predicted areal density, based on varying yarn linear density and the number of picks in the free and bonded wall, was compared to experimental results and showed good agreement. A geometric model was derived to predict the areal density of 3D woven honeycomb preforms for producing the composite liner of aircrew helmet. The model was validated using experimental data from a variety of 3D woven honeycomb preforms, showing that it accurately predicts the areal density and can be used as a tool to design 3D woven honeycomb preforms for advanced composites.

Numerical simulation of tensile, flexural, and impact behavior of three dimensional orthogonal hybrid jute/HTPET woven fabric composites

3D textile reinforced composites have some advantages over common 2D composites due to their unique mechanical properties. In this research, five composite samples with different percentages of jute and high tenacity polyethylene terephthalate fibers in the form of three-dimensional orthogonal woven fabrics were designed by TexGen software in meso and macro scales, and their tensile, flexural, and impact behaviors were analyzed by the finite element method in Abaqus software. The results were in good agreement with the experimental results. The mean errors between experimental and numerical results were 11% for tensile strain, 9% for tensile strength, 8% for Young’s modulus, 9.8% for tensile toughness, 6% for elastic flexural energy, 5.7% for flexural modulus, and 19.8% for impact resistance.

Bioinspired Design Rules from Highly Mineralized Natural Composites for Two-Dimensional Composite Design.

Discoveries of two-dimensional (2D) materials, exemplified by the recent entry of MXene, have ushered in a new era of multifunctional materials for applications from electronics to biomedical sensors due to their superior combination of mechanical, chemical, and electrical properties. MXene, for example, can be designed for specialized applications using a plethora of element combinations and surface termination layers, making them attractive for highly optimized multifunctional composites. Although multiple critical engineering applications demand that such composites balance specialized functions with mechanical demands, the current knowledge of the mechanical performance and optimized traits necessary for such composite design is severely limited. In response to this pressing need, this paper critically reviews structure-function connections for highly mineralized 2D natural composites, such as nacre and exoskeletal of windowpane oysters, to extract fundamental bioinspired design principles that provide pathways for multifunctional 2D-based engineered systems. This paper highlights key bioinspired design features, including controlling flake geometry, enhancing interface interlocks, and utilizing polymer interphases, to address the limitations of the current design. Challenges in processing, such as flake size control and incorporating interlocking mechanisms of tablet stitching and nanotube forest, are discussed along with alternative potential solutions, such as roughened interfaces and surface waviness. Finally, this paper discusses future perspectives and opportunities, including bridging the gap between theory and practice with multiscale modeling and machine learning design approaches. Overall, this review underscores the potential of bioinspired design for engineered 2D composites while acknowledging the complexities involved and providing valuable insights for researchers and engineers in this rapidly evolving field.

Fully automated 3D body composition analysis and its association with overall survival in head and neck squamous cell carcinoma patients.

We developed a method for a fully automated deep-learning segmentation of tissues to investigate if 3D body composition measurements are significant for survival of Head and Neck Squamous Cell Carcinoma (HNSCC) patients. 3D segmentation of tissues including spine, spine muscles, abdominal muscles, subcutaneous adipose tissue (SAT), visceral adipose tissue (VAT), and internal organs within volumetric region limited by L1 and L5 levels was accomplished using deep convolutional segmentation architecture - U-net implemented in a nnUnet framework. It was trained on separate dataset of 560 single-channel CT slices and used for 3D segmentation of pre-radiotherapy (Pre-RT) and post-radiotherapy (Post-RT) whole body PET/CT or abdominal CT scans of 215 HNSCC patients. Percentages of tissues were used for overall survival analysis using Cox proportional hazard (PH) model. Our deep learning model successfully segmented all mentioned tissues with Dice's coefficient exceeding 0.95. The 3D measurements including difference between Pre-RT and post-RT abdomen and spine muscles percentage, difference between Pre-RT and post-RT VAT percentage and sum of Pre-RT abdomen and spine muscles percentage together with BMI and Cancer Site were selected and significant at the level of 5% for the overall survival. Aside from Cancer Site, the lowest hazard ratio (HR) value (HR, 0.7527; 95% CI, 0.6487-0.8735; p = 0.000183) was observed for the difference between Pre-RT and post-RT abdomen and spine muscles percentage. Fully automated 3D quantitative measurements of body composition are significant for overall survival in Head and Neck Squamous Cell Carcinoma patients.

Mechanical performance of 3D woven glass fiber I-beam composites with in-situ polyurethane foaming

3D weaving of I-beam structures can potentially create delamination- and joint-free structures, expanding their use in engineering applications compared to metal or traditional laminated composite beams. In addition, polymeric foams can be utilized to fill the vacancies between the web and the flanges of I beams, improving the mechanical characteristics and the structural integrity with little to no weight penalty. Moreover, interposing an adhesive layer between the I beam and the foam structure can result in a more effective bonding which intensifies the structure's robustness. In this study, high-performance I-beam composites were produced by combining polymeric foams with 3D woven glass fiber composites. Low- and high-density polyurethane foams were successfully inserted between the web and the flanges of 3D woven glass fiber composites manufactured by the vacuum infusion process using the free-rise foaming method. Samples with adhesive films were also produced to assess and compare their effectiveness with the composites made solely of polyurethane foam and I beam. The increases in energy absorption capacity and compressive and flexural properties were analyzed under compressive and flexural (three-point bending) loading. The obtained results indicate that structural integrity under bending can be substantially improved with the in-situ foaming supported by adhesive layers.

Directing Molecular Weaving of Covalent Organic Frameworks and Their Dimensionality by Angular Control.

Although reticular chemistry has commonly utilized mutually embracing tetrahedral metal complexes as crossing points to generate three-dimensional molecularly woven structures, weaving in two dimensions remains largely unexplored. We report a new strategy to access 2D woven COFs by controlling the angle of the usually linear linker, resulting in the successful synthesis of a 2D woven pattern based on chain-link fence. The synthesis was accomplished by linking aldehyde-functionalized copper(I) bisphenanthroline complexes with bent 4,4'-oxydianiline building units. This results in the formation of a crystalline solid, termed COF-523-Cu, whose structure was characterized by spectroscopic techniques and electron and X-ray diffraction techniques to reveal a molecularly woven, twofold-interpenetrated chain-link fence. The present work significantly advances the concept of molecular weaving and its practice in the design of complex chemical structures.

A meso-scale stochastic model for tensile behavior of 2D woven ceramic composites considering void defects and stacking mode

A sophisticated meso-scale model is developed based on finite element method to study the tensile failure behavior of 2D-SiCf/SiC. To this end, a thoroughgoing characterization was carried out using SEM and micro-CT to obtain the microstructural parameters, and a progressive damage model related to matrix cracking was proposed for fiber tows to depict the matrix brittleness, an essential feature of ceramic matrix composites. Thereby, a sophisticated model comprehensively incorporating void defects, layer shifts and the damage induced by matrix cracking was established. Elaborate experimental tests including digital image correlation, acoustic emission (AE) and high-speed observation were synthetically conducted to validate this model, and results indicated that this model could provide an accurate prediction. Especially, micro and meso-scale damage modes were identified and the damage process was thoroughly analyzed by combing this model and AE technology. The numerical studies were then implemented to evaluate the effect of stacking mode and porosity.

Discovery of 2D Materials using Transformer Network‐Based Generative Design

Two‐dimensional (2D) materials offer great potential in various fields like superconductivity, quantum systems, and topological materials. However, designing them systematically remains challenging due to the limited pool of fewer than 100 experimentally synthesized 2D materials. Recent advancements in deep learning, data mining, and density functional theory (DFT) calculations have paved the way for exploring new 2D material candidates. Herein, a generative material design pipeline known as the material transformer generator (MTG) is proposed. MTG leverages two distinct 2D material composition generators, both trained using self‐learning neural language models rooted in transformers, with and without transfer learning. These models generate numerous potential 2D compositions, which are plugged into established templates for known 2D materials to predict their crystal structures. To ensure stability, DFT computations assess their thermodynamic stability based on energy‐above‐hull and formation energy metrics. MTG has found four new DFT‐validated stable 2D materials: NiCl4, IrSBr, CuBr3, and CoBrCl, all with zero energy‐above‐hull values that indicate thermodynamic stability. Additionally, GaBrO and NbBrCl3 are found with energy‐above‐hull values below 0.05 eV. CuBr3 and GaBrO exhibit dynamic stability, confirmed by phonon dispersion analysis. In summary, the MTG pipeline shows significant potential for discovering new 2D and functional materials.

An effective multiscale analysis for the mechanical properties of 3D braided composites considering pore defects

An effective multiscale analysis for the mechanical properties of 3D braided composites considering pore defects