Advanced Composite Structures Research Articles

Analysis of the geometric non-uniformity effect on the free vibration characteristic of graphene reinforced axially functionally graded nano-composite beam

In this research work, an axially functionally graded (AFG) Nano-composite beam is modeled to observe the free vibration behavior beamfor non-uniform geometry. The beam is mathematically modeled as an Euler-Bernoulli beam using MATLAB code. Where the graphene Nanoplatelets (GPL) are axially reinforced with A and V type functions. The geometry nonuniformity in the beam is introduced with an exponential function, where the exponent of the equation governs the cross-section of the geometry. The material modulus and other properties at each section along the beam axis are modeled as particulate fiber randomly oriented composite with the help of Halpin Tsai theory and rule of mixture. The finite element method (FEM) is used for the solution and analysis of the model. The model is first validated for uniform geometry results then the non-dimensional fundamental frequency of the AFG beam is obtained for various parameter combinations. The results for various combinations are plotted and it is noted that the free vibration characteristic of the model is strongly dependent on non-uniformity in geometry, end-support constraints,and slenderness ratios (L/h0) combination of the model. This research contributes significantly to the understanding of advanced composite structures and offers potential benefits for the design and optimization of these materials in various engineering applications. The study reveals significant variations in natural frequencies depending on the A type & V type GPL distribution pattern and geometric parameters, which are crucial for the structural integrity and performance of nano composite beams.

Biaxial tubular hybrid braided structures with supreme tensile performance: Experimental and statistical assessments

Biaxial tubular braided structures are increasingly being recognized as superior alternatives to traditional materials in structural engineering due to their enhanced tensile properties and durability. In this study, the mechanical characteristics of these structures are examined, focusing on the influence of braiding angle, yarn hybridization, and layer configurations on tensile behavior. Sixteen diverse specimens, crafted from polyester and basalt yarns using a 32-carrier vertical braiding machine, are explored, incorporating variations across four braiding angles, three hybridizations, and both single and dual-layer setups. Each specimen underwent rigorous uniaxial tensile testing, revealing critical insights into the relationships between structural configuration and mechanical performance. It was found that increasing the braiding angle significantly enhances elongation, strain, and energy absorption capacities. Additionally, a higher basalt yarn content was observed to amplify tensile strength, suggesting potential for tailored structural optimization. Notably, dual-layer configurations were found to outperform single layers in tensile efficiency, underscoring the advantage of layered designs in engineering applications. A significant gap in current literature is filled by this study, as a detailed analysis of biaxial tubular hybrid braids is provided, paving the way for their informed application in advanced composite structures.

Nonlinear vibrations of three-phase composite truncated conical shells affected by complex factors under 1:1 internal resonance

The investigation into the nonlinear vibrations of the three-phase composite truncated conical shell structure is still in its early stages, which limits its practical implementation. This paper involves calculating equivalent physical property parameters for the three-phase composite materials and establishing a nonlinear dynamic model for the truncated conical shells. The study examines the nonlinear vibration characteristics of the shells under various conditions, considering factors such as temperature, humidity, reinforcement components, and external excitation. It is observed that temperature and humidity changes have significant influences on the structure's resonant responses, with varying effects for the driving mode and accompanying mode. The addition of GPLs or carbon fiber as reinforcement components weakens the resonant response amplitude with an increase in their content. External excitation is identified as a noteworthy energy input source that significantly impacts the nonlinear vibration characteristics of the three-phase composite conical shell structure, thereby amplifying the resonant responses and complicating the nonlinear dynamic behaviors. The findings hold significant value for guiding the design of advanced composite structures in future engineering.

Machinability characteristics study on hibiscus rosa-sinensis reinforced polymer composites using soft computing techniques

Abstract Understanding the drilling behaviour of composite materials is crucial for optimizing their manufacturing processes and enhancing their applicability across various industries. In this study, the drilling process of Hibiscus Rosa-Sinensis Polymer matrix composites is investigated due to the significance of investigating such advanced and sustainable composite materials for their potential applications. Hibiscus Rosa-Sinensis Fibers are extracted and processed from the outer bark of the hibiscus plant, are incorporated into polymer matrices in varying weight percentages (0 Wt%, 10 Wt%, 20 Wt%) to form discontinuously reinforced polymer composites. Samples with uniform dimensions of 150 × 75 × 15 mm, are used for the drilling operation using Ace Micromatic DTC-400 instrument. The project focuses on analysing the influence of key drilling input parameters such as Spindle speed, Feed rate and HRS (Hibiscus Rosa-Sinensis) Fiber weight percentage on the Thrust Force (N) and Torque (N-m) generated during drilling operations. Taguchi’s Design of Experiments with L27 orthogonal array is used to systematically optimize the input parameters to gain insights into the drilling behaviour of these composite materials. Further a second order mathematical model has been generated for Thrust Force and Torque using Response Surface Methodology (RSM). Thrust Force and Torque during drilling are measured using 9257 BA KISTLER Dynamometer coupled with DynoWare 2825 A software. The findings of this study not only contribute to a deeper understanding of the drilling process but also hold significant implications for industries reliant on composite materials. From aerospace to automotive sectors, where lightweight and durable materials are essential, to construction and renewable energy industries seeking sustainable alternatives, the application potential of hibiscus Rosa-Sinensis Fiber-reinforced composites is vast. By elucidating the intricate dynamics of drilling operations on these materials, this research paves the way for enhanced manufacturing processes and the development of advanced composite structures tailored to meet the demands of diverse industrial applications.

Real-scale tests on bundle-bar-filled circular hollow sections (BBFC) under eccentric compression

The escalating demand for innovative, multifunctional, and advanced composite structures stems from the pressing concerns related to cost efficiency and environmental impact, alongside interest in the prefabrication methods within the construction industry. This paper advances a pioneering approach by proposing the utilization of high-strength bundle-bar-filled circular hollow sections (BBFC) as columns. In this method, a bundle comprising high-strength reinforcing bars with an impressive yield strength of 670 N/mm² is strategically placed within a steel tube, varying in related-slenderness values from 0.86 to 2.44. Subsequent to this arrangement, the bundle undergoes grouting with mortar, resulting in the formation of the BBFCs. The composition of the bundle is a critical aspect, with different configurations featuring 1, 3, 7, and 19 high-strength bars. Given the intricacies involved in the fabrication process, this paper meticulously outlines the unique techniques employed to successfully manufacture the BBFCs. To comprehensively evaluate the performance of these innovative columns, a series of ten real-scale tests were conducted. These tests look at various aspects, including capacity, flexural stiffness, deflection, and strain response. In addition to experimental validation, finite element analysis was applied, not only to corroborate the empirical findings but also to lay the foundation for future parametric studies. These subsequent investigations aim to explore and understand the influence of different geometric and material features on the structural behaviour of this innovative composite column, thereby contributing to the advancement of knowledge in the field.

Generalized Background Vector Method for Tailored Three-Dimensional Nonperiodic Woven Composite Designs

In critical aerospace applications such as Ceramic Matrix Composite structures, novel three-dimensional nonperiodic textile composite preforms hold great promise for creating advanced composite structures with strategically aligned fiber tows. However, the inherently challenging nature of determining tow locations, a large-scale combinatorial problem, presents a significant obstacle in textile architecture design. This study generalizes the previously developed Background Vector Method (BVM) to address diverse design requirements and constraints, effectively circumventing combinatorial design complexities via a game theoretic approach. This approach allows for the creation of tunable designs for woven architectures with complex geometries, such as channels and tapering features, through simple control parameter adjustments. The method demonstrates exceptional computational efficiency, making it suitable for large-scale nonperiodic textile structures. Case studies including woven sandwich and airfoil structures highlight the generalized BVM’s versatility and effectiveness in addressing complex design challenges within the aerospace sector.

AI optimization and mathematical simulation validated by nondestructive testing for resonance frequency in advanced composite structures for bridge applications

This paper presents a novel approach integrating artificial intelligence (AI) optimization and mathematical simulation to predict the resonance frequency of nanoclay-reinforced concrete cylindrical shell structures intended for bridge applications. These composite structures, known for their enhanced mechanical properties, require precise evaluation of their vibrational behavior to ensure structural stability and longevity. Traditional methods for predicting resonance frequencies are often time-consuming and prone to inaccuracies, especially in complex materials like nanoclay composites. To address this, an AI-based optimization algorithm was developed, incorporating Particle Swarm Optimization (PSO) and a mathematical modeling to simulate resonance characteristics under varying material and geometric parameters. The mathematical modeling is validated using nondestructive testing (NDT) techniques, such as modal analysis, which provided real-world resonance data without damaging the structure. The nondestructive testing results is compared against the mathematical model to ensure accuracy and reliability. The integration of nanoclay into the concrete matrix significantly altered the vibrational properties, enhancing the stiffness and reducing damping losses, which is crucial for bridge applications where dynamic loads are prevalent. The optimized model not only predicted resonance frequencies with high accuracy but also demonstrated its potential for large-scale bridge infrastructure. This methodology offers a streamlined and robust tool for engineers, reducing the need for physical prototyping and providing enhanced design capabilities. Future research will focus on expanding the model to incorporate additional material behaviors and load conditions, furthering its application in civil engineering.

On the measurement of the nonlinear dynamics of sandwich sector plate surrounded by the auxetic concrete foundation: Introducing a machine learning algorithm for nonlinear problems

The measurement of the nonlinear dynamics of sandwich rotary sector plates is crucial for measurement engineers as it enables the precise analysis and understanding of the behavior of advanced composite structures under dynamic conditions. These measurements help in identifying and mitigating potential issues related to vibration, stability, and fatigue, which are critical in industries like aerospace, automotive, and civil engineering. Accurate data on nonlinear dynamics aids in the optimization of design, enhancing performance, durability, and safety. Furthermore, it supports the development of predictive maintenance strategies, reducing downtime and operational costs, and contributing to the advancement of engineering materials and techniques. So, in the current work, for the first time, nonlinear dynamics of sandwich rotary sector plate via a mathematical modeling and machine learning algorithm is presented. First, due to a lack of information on the nonlinear dynamics of sandwich rotary sector plates, a dataset for training, testing, and validating the machine learning algorithm is presented. For this purpose, using Hamilton’s principle, Von-Karman nonlinearity, and first-order shear deformation theory (FSDT), the nonlinear governing equations are obtained. Also, due to increasing stiffness and stability, an auxetic concrete foundation covers the sandwich structure. After that using the two-dimensional generalized differential quadrature method (GDQM) and Newmark’s time integration method (NTIM), the nonlinear equations are solved. This research provides valuable insights for engineers in designing advanced composite structures with improved dynamic properties. The results also contribute to the broader understanding of nonlinear dynamic interactions in complex material systems, paving the way for innovative applications in various engineering fields.

Investigating resonance frequency in advanced composite structures as the main part of bridge construction: a multi-physics simulation approach

This paper investigates the nonlinear vibration characteristics and resonance frequencies of a graphene oxide powders (GOP) reinforced beam structure, which is in contact with fluid, serving as a crucial component in bridge construction. Utilizing a multi-physics simulation approach, this study integrates both mathematical modeling and COMSOL simulations to analyze the behavior of the GOP-reinforced beam. The fluid in the analysis is modeled as incompressible, non-viscous, and irrotational, contributing to the interaction with the beam structure. The isogeometric approach, coupled with the harmonic balance method and the arc-length technique, is employed to solve the complex nonlinear partial differential equations (PDEs). This coupled method ensures the accurate capture of the nonlinear response and resonance frequencies of the beam, which are critical for the safe design and performance of bridge structures. The findings reveal the significant influence of GOP reinforcement on the vibration and stability characteristics of the beam, highlighting its potential to enhance structural performance under dynamic loading conditions. The study also emphasizes the importance of considering fluid-structure interactions in the design process, as the fluid’s presence substantially affects the vibration behavior. This research contributes to the understanding of the dynamic behavior of advanced composite structures in fluid environments, offering valuable insights for the design and analysis of bridge components. The results demonstrate the efficacy of the proposed simulation approach in addressing the complexities of nonlinear vibrations in fluid-structure interaction systems, paving the way for future developments in civil engineering applications.

Influence of Ceramic Size and Morphology on Interface Bonding Properties of TWIP Steel Matrix Composites Produced by Lost-Foam Casting

This study investigated the fabrication and characterization of large ceramic-reinforced TWIP (twinning-induced plasticity) steel matrix composites using the lost-foam casting technique. Various ceramic shapes and sizes, including blocky, flaky, rod-like, and granular forms, were evaluated for their suitability as reinforcement materials. The study found that rod-like and granular ceramics exhibited superior structural integrity and formed strong interfacial bonds with the TWIP steel matrix compared to blocky and flaky ceramics, which suffered from cracking and fragmentation. Detailed microstructural analysis using scanning electron microscopy (SEM) and industrial computed tomography (CT) revealed the mechanisms influencing the composite formation. The results demonstrated that rod-like and granular ceramics are better for reinforcing TWIP steel composites, providing excellent mechanical stability and enhanced performance. This work contributes to the development of advanced composite structures with potential applications in industries requiring high-strength and durable materials.

Machine Learning Algorithms for Prediction and Characterization of Cohesive Zone Parameters for Mixed-Mode Fracture

Fatigue and fracture prediction in composite materials using cohesive zone models depends on accurately characterizing the core and facesheet interface in advanced composite sandwich structures. This study investigates the use of machine learning algorithms to identify cohesive zone parameters used in the fracture analysis of advanced composite sandwich structures. Experimental results often yield non-unique solutions, complicating the determination of cohesive parameters. Numerical determination can be time-consuming due to fine mesh requirements near the crack tip. This research evaluates the performance of Support Vector Regression (SVR), Random Forest (RF), and Artificial Neural Network (ANN) machine learning methods. The study uses features extracted from load–displacement responses during the fracture of the Asymmetric Double-Cantilever Beam (ADCB) specimen. The inputs include the displacement at the maximum load (δ*), the maximum load (Pmax), the total area under the load–displacement curve (At), and the initial slope of the linear region of the load–displacement curve (m). There are two objectives in this research: the first is to investigate which method performs best in identifying the interfacial cohesive parameters between the honeycomb core and carbon-epoxy facesheets, while the second objective is to reduce the dimensionality of the dataset by reducing the number of input features. Reducing the number of inputs can simplify the models and potentially improve the performance and interpretability. The results show that the ANN method produced the best results, with a mean absolute percentage error (MAPE) of 0.9578% and an R-squared (R²) value of 0.7932. These values indicate a high level of accuracy in predicting the four cohesive zone parameters: maximum normal contact stress (σI), critical fracture energy for normal separation (GI), maximum equivalent tangential contact stress (σII), and critical fracture energy for tangential slip (GII).

Multimodal data fusion enhanced deep learning prediction of crack path segmentation in CFRP composites

Carbon fiber-reinforced polymer (CFRP) composites are extensively used in various engineering applications due to their superior strength-to-weight ratio and excellent mechanical properties. Predicting crack propagation paths in CFRP composites is a complex challenge due to their multiphase nature and intricate microstructural interactions. While finite element (FE) simulations possess significant capabilities for this purpose, they entail substantial computational demands and extended execution times, thereby limiting their viability in applications with high computational requirements. To address this challenge, we propose an end-to-end deep learning framework specifically for predicting crack propagation paths in two-dimensional CFRP composites. Drawing inspiration from semantic segmentation techniques, we employ EfficientNet for feature extraction, enabling the capture of hierarchical and multiscale features from both microstructure images and stress field distributions. A key aspect of our framework is the utilization of multimodal data fusion and self-attention mechanisms to effectively integrate these diverse data sources. The results demonstrate the effectiveness of our multimodal feature integration approach, producing accurate segmentations of crack path. This novel framework offers a promising approach to understanding and predicting failure mechanisms in composite materials, with significant implications for the design and maintenance of advanced composite structures.

Acoustic emission monitoring for damage diagnosis in composite laminates based on deep learning with attention mechanism

During service, advanced composite materials are susceptible to various forms of damage. Developing an efficient real-time Structural Health Monitoring (SHM) method is of profound significance to ensure the normal operational integrity of the advanced composite structures. This study introduces a novel end-to-end SHM approach employing acoustic emission (AE) techniques and a deep learning model with attention mechanisms (AM) to automate the damage diagnosis process and improve prediction accuracy. Low-velocity impact (LVI) experiments were carried out, recording AE signals for subsequent bi-spectrum analysis. Three dedicated AM-based Convolutional Neural Network (CNN) architectures were designed for damage diagnosis, utilizing 2D bi-spectrum contour images as input data. A comparison was made between the proposed CNN model, deep learning and traditional machine learning models in the context of damage diagnostics, revealing that CNN models combined AM exhibit significantly higher accuracy in pattern recognition tasks. The accuracies of the baseline CNN, self-attention-CNN, multi-head attention-CNN and convolutional block attention module-CNN are 98.22%, 98.50%, 98.85%, and 98.87%, respectively. The CNN embedded with AM demonstrated superior predictive performance and fewer instances of misclassification. Considering the specific demands of damage diagnostics in composite materials, the CBAM-CNN is better suited for composite material health monitoring tasks.

On the transient vibrations of the advanced composite systems under mechanical loading

This work examines the transient nonlinear oscillations of complex composite systems under mechanical pressure utilizing Reddy’s theory of higher-order shear deformation plates. The analysis entails the formulation of nonlinear equations that regulate the stress and displacement fields inside the composite structure. The dynamic behavior is defined by using Hamilton’s idea. The transient response of the composite system under various loading conditions is determined by deriving analytical solutions. The work offers a full knowledge of the dynamic behavior of advanced composite structures by using Reddy’s higher-order shear deformation plate theory, which takes into account the effects of transverse shear deformation and rotating inertia. The intricate interplay of material properties, shape, and external force is elucidated by the nonlinearity inherent in the equations. This provides valuable information on the temporary vibrations that occur in the composite system. The work employs rigorous mathematical analysis and closed-form solutions to clarify the intricate transient dynamics of modern composite structures, providing insights into how they react to mechanical loads over time. The results enhance the overall comprehension of composite material behavior and provide useful insights for the technical design and optimization of composite structures.

Impact Performance of 3D Orthogonal Woven Composites: A Finite Element Study on Structural Parameters

This study uses the finite element method (FEM) to investigate the effect of key structural parameters on the impact resistance of E-glass 3D orthogonal woven (3DOW) composites subjected to low-velocity impact. These structural parameters include the number of y-yarn layers, the path of the binder yarn (z-yarn), and the density of the x-yarn. Using ABAQUS, yarn-level finite element (FE) models are created based on the measured geometrical parameters and validated for energy absorption and damage behavior from experimental data gathered from the previous study. The results from finite element analysis (FEA) indicate that the x-yarn density and the binder path substantially influenced the composites’ damage behavior and impact performance. Increasing x-yarn density in 3DOW leads to a 15% increase in energy absorption compared to models with reduced x-yarn densities. Moreover, as the x-yarn density increases, crack lengths at the back face of the resin matrix decrease in the y-yarn direction but increase in the x-yarn direction. The basket weave structure absorbs less energy than plain and 2 × 1 twill structures due to the less constrained weft primary yarns. These results underscore the importance of these structural parameters in optimizing 3DOW composite for better impact performance, providing valuable insights for the design of advanced composite structures.

Optimal Molecular Dynamics System Size for Increased Precision and Efficiency for Epoxy Materials.

Molecular dynamics (MD) simulation is an important tool for predicting thermo-mechanical properties of polymer resins at the nanometer length scale, which is particularly important for efficient computationally driven design of advanced composite materials and structures. Because of the statistical nature of modeling amorphous materials on the nanometer length scale, multiple MD models (replicates) are typically built and simulated for statistical sampling of predicted properties. Larger replicates generally provide higher precision in the predictions but result in higher simulation times. Unfortunately, there is insufficient information in the literature to establish guidelines between MD model size and the resulting precision in predicted thermo-mechanical properties. The objective of this study was to determine the optimal MD model size of epoxy resin to balance efficiency and precision. The results show that an MD model size of 15,000 atoms provides for the fastest simulations without sacrificing precision in the prediction of mass density, elastic properties, strength, and thermal properties of epoxy. The results of this study are important for efficient computational process modeling and integrated computational materials engineering (ICME) for the design of next-generation composite materials for demanding applications.

Artificially intelligent process planner for automated fiber placement

Automated Fiber Placement (AFP) is a critical process in the manufacturing of advanced composite structures. The accurate and efficient placement of fibers plays a vital role in determining the final strength and integrity of the composite part. This paper presents an artificially intelligent process planner designed to optimize and automate the fiber placement process. The system utilizes advanced machine learning techniques to analyze design specifications, simulate fiber placement scenarios, and generate optimized placement plans. Through the integration of artificial intelligence, this process planner aims to enhance the speed, accuracy, and reliability of AFP, leading to improved composite structures.

A digital environment for simulating the automated fiber placement manufacturing process

Process parameters in manufacturing have proven to be critical attributes that directly influence defect formation and quality of a finished product. In automated fiber placement, the variability of tool surface geometry makes the understanding of process parameter behavior especially important. Numerous publications have developed models to understand compaction pressure and heat application; however, efforts are typically created for one manufacturing scenario using a single tool surface. To address the present gap, this work presents a novel process simulation software for automated fiber placement, providing a unified platform for multiple predictive models. Initial development of the software is introduced by discussing software design and the creation of two process models. A brief demonstration of the software is then provided by showcasing the results of different analysis schemes using both process models. This software emerges as a key tool in advancing the precision and efficiency of automated fiber placement, offering a versatile solution to model diverse manufacturing scenarios and significantly enhance product integrity.

Nonlinear resonant responses and chaotic dynamics of three-dimensional braided composite cylindrical shell

The advanced three-dimensional braided composite structure has excellent integrity, which can effectively avoid the problems of interlayer stress jump and interface debonding that may occur in traditional composite laminated structures. However, most of the existing references on the three-dimensional braided composite structure are fragmented, and there is little research on their vibrations. Taking the cylindrical shell of a rocket as the object and considering the complex flight environment, a general framework for the vibration problems of the three-dimensional braided composite shell is proposed for the first time. The equivalent mechanical parameters of the three-dimensional braided composite material are calculated based on the volume averaging method. Then, the nonlinear dynamic model of the three-dimensional braided composite cylindrical shell is established, and the natural vibration characteristics are analyzed. Under the case of 1:1 internal resonance, the pseudo-arc length continuation method is used to solve the nonlinear resonant responses of this nonlinear system. Finally, we apply the fourth-order Runge-Kutta algorithm to study the chaotic dynamics of the three-dimensional braided composite cylindrical shell model. This framework presented in this paper can be extended to the study of nonlinear vibrations and chaotic dynamics of other three-dimensional braided composite structures.

A novel systematic approach for robust numerical simulation of carbon fiber-reinforced plastic circular tubes: Utilizing machine-learning techniques for calibration and validation

Developing a reliable and robust finite element model of a carbon fiber-reinforced plastic (CFRP) composite structure is investigated by using the LS-DYNA solver and Python. This study tries to provide a systematic numerical approach to cover the principal impediment to adaptation of composite energy absorbers, that is the lack of a reliable predictive method. The proposed procedure aims to further the understanding of advanced composite structures’ behavior during the crash phenomenon by developing an accurate finite element model. To do so, the mechanical properties of the material were extracted from American Society for Testing and Materials (ASTM) standard test methods, followed by experimental investigation of circular CFRP tubes undergoing quasi-static loading. A numerical simulation framework was then utilized to scrutinize the effectiveness of simulation parameters on the crushing mechanism. Finally, a systematic approach based on machine learning techniques was performed to adjust non-physical modeling parameters for further calibration and validation. In this regard, a versatile Python code was developed to automate pre-processing, processing, and post-processing steps. The code also provides a groundwork to perform machine learning techniques. Interestingly, the numerical and experimental results were highly correlated with a correlation coefficient of almost 90%. Additionally, several non-physical numerical parameters were found to be inactive, while some else were identified as effective parameters, and their corresponding effectiveness was quantitatively extracted and discussed for the first time in the literature.