Complex Composite Structures Research Articles

Sensor placement optimization for composite aircraft structures: A multi-objective Kriging-based approach

Efficient sensor placement is critical for optimizing structural health monitoring (SHM) systems, particularly for composite aircraft structures. The challenge lies in determining the optimal number and positions of sensors to balance monitoring accuracy with resource efficiency. This study proposes a novel multi-objective optimization methodology that integrates Kriging interpolation for mode shape reconstruction with the Lichtenberg algorithm for sensor placement optimization. The approach minimizes the number of sensors while maximizing signal quality and minimizing interpolation error. The methodology is validated on both composite plates and the main rotor blade of an AS-350 helicopter using finite element modal analysis data. The results demonstrate significant reductions in the number of sensors required while maintaining high accuracy in capturing the structure’s dynamic response, showing the effectiveness of the proposed method for SHM applications in complex composite structures.

Design of China's Modern Parks in Post-industrial Territories: Project Approaches in the Processing of Industrial Heritage

The transformation of industrial heritage is an essential issue in the urban development process in China. Currently, most of China’s industrial heritage has undergone numerous changes. Such territories, where industrial buildings are stored, have undergone adaptive changes for recreational use. Therefore, the purpose of the research was to establish project approaches to the processing of industrial heritage and the formation of modern Chinese parks in post-industrial territories. Research methods. Achieving the goal of this work involves studying the object and identifying various types of connections that exist in it, which determines the use of a systemic approach. In the process of conducting the research, both the comparative-historical method and the morphological and compositional methods were used. Their inclusion in the research procedure made it possible to consider the park environment as a complex compositional structure characterised by the interrelationship between functional and morphological solutions and the artistic characteristics of park environment design. Scientific novelty. For the first time, the work systematises material on the usage of industrial heritage objects in the design of modern parks created on post-industrial territories. Project approaches to its development have been established. Conclusions. The contem porary approach in the processes of renewal of industrial heritage is characterised by the transition from only adaptive reuse to the creation of urban cultural landscapes. This is due to combining the original industrial texture with the new design, which forms a landscape with unique characteristics. Scale is the most characteristic feature of industrial heritage. This scale marks industrial buildings and individual equipment that characterises a particular territory. The following project approaches have been established in the processing of buildings: exposing the frameworks of industrial buildings while preserving their authentic appearance, when the brutal textures of building materials of industrial buildings act as carriers of memory; another approach – frame structures in the environment of the park landscape are solved as expressive accents thanks to bright painting, lighting, wrapping with a glass shell. Also, an expressive object of industrial heritage is the equipment that ensured the production process in each production. Depending on the type of this equipment, several approaches are distinguished in its processing: large-sized objects retain their original appearance, are brightly painted, and play the role of visual anchors in the park environment, marking the identity of the area; as for small individual elements of production equipment, they become art objects, organising specific areas of the park.

SH‐waves in a complex fluid composite structure with spring membrane imperfect interfaces

AbstractComplex fluids are of nobleness due to their unique mechanical responses to applied stress, which are different from simple fluids. Therefore, the current study encapsulated the behavior of SH‐wave in a complex fluid layer overlying a fiber‐reinforced half‐space with six different cases considered at the common interface. These cases involve various interface conditions, including perfect bonding, a spring layer, an infinitely thin membrane layer, loose bonding, a combined spring‐membrane layer, and a membrane layer with loose bonding. Precisely, the effect of loose bonding, membrane strength, and spring stiffness at the common interface on the propagation of SH (Shear Horizontal) wave is studied in detail. The problem is mathematically formulated for all the aforementioned six models followed by their solution with the aid of appropriate boundary conditions. Numerical computations are performed with a view to study the effect of stiffness of spring and membrane, as well as loose bonding at the common interface of the media. It is demonstrated that membrane strength impedes wave propagation, while increased spring stiffness supports it. Notably, the combined spring‐membrane interface has a more pronounced effect than a spring layer alone. The findings of this study may be utilized in the development of membrane technology, including the study of fluid membranes and it has significant importance in seismic engineering and vibration isolation systems.

The impact of 45S5 bioglass vs. β-TCP nanoparticles ratio on rheological behavior of formulated printing inks and 3D printed polycaprolactone-based scaffolds final properties

Extrusion based 3-D printing has been extensively applied to create geometrically complex composite polymer-ceramic structures as bone tissue substitute. The rheological features of the formulated bioink that regulate the printability and resolution of the printed scaffolds, rely on physicochemical properties of ink components, mainly their composition and chemical structure. The aim of this study was to evaluate the effect of different content of 45S5 bioglass (BG) and β-tricalcium phosphate (β-TCP) nanoparticles on the rheological behavior of printing inks and final composite scaffolds based on polycaprolactone (PCL)/BG/β-TCP. Ceramic nano-powders were first characterized and the composite bioinks were prepared by mixing various ratios of BG and β-TCP powders into the 50% w/v of PCL solution (β-TCP/BG: 70/30, 50/50, 30/70, and 10/90 w/w %). All formulated inks showed a thixotropic behavior and viscosity significantly increased by applying higher fraction of BG. Interestingly highly loaded β-TCP ink (β-TCP/BG: 70/30) revealed a rubbery nature with high surface tension which reduced final scaffolds resolution. All printed scaffolds possessed highly porous structure with interconnected pores. However, porosity percentage and shape stability improved by increasing BG content which was accompanied with lower mechanical strength and superior biodegradation and bioactivity of scaffolds. The biological performance of the printed scaffolds was evaluated using osteoblast cell line MG-63. MTT assay and cell attachment observation by SEM confirmed that printed scaffolds are well biocompatible and properly support cell colonization and proliferation. In overall, besides more appropriate materialistic properties, scaffolds with β-TCP/BG: 30/70 composition provided the most favorable microenvironment for cell colonization and growth.

Effect of carbon nanotube on the mechanical properties of short carbon fiber reinforced polyether-ether-ketone

Additive manufacturing provides a quick and cost-effective way for the fabrication of complex composite structures. Additively manufactured short carbon fiber reinforced polyether-ether-ketone (SCF/PEEK) is a promising thermoplastic composite material due to its excellent designability, ease of manufacture, and good recyclability. However, the mechanical properties of 3D printed SCF/PEEK are unsatisfactory due to the layer-by-layer forming process. In this study, CNTs were introduced to 3D printed SCF/PEEK, and the influence mechanism of CNTs on the mechanical properties was analyzed and discussed in depth. The results showed that the introduction of CNTs could effectively enhance the tensile modulus of 3D printed SCF/PEEK, whilst it led to a decrease in the tensile strength and interlaminar shear strength of the composites. This was mainly related to the poor dispersion and agglomeration of CNTs. The modulus of 3D printed SCF/PEEK reached about 5.17 GPa, representing a 95.1% increase.

Calculating shear lag in steel-concrete composite beams under combined compression and bending

Long and complex composite steel-concrete structures are becoming common, requiring a deep understanding of the effects induced by the simultaneous action of axial forces and bending. In fact, the axial force generated, for instance, by cable inclination in cable-supported structures can modify the stress distribution within the elements compared to bending scenarios, thereby necessitating a revision of the effective width to be utilized. Nonetheless, current design codes, including Eurocode specifications and others, lack provisions for addressing the combined effects of axial force and bending, as they are exclusively tailored for bending. This limitation can introduce design complexities, necessitating the implementation of intricate Finite Element (FE) models, which impose substantial computational loads and design efforts. The methodology proposed in this paper overcomes these challenges allowing to assess the stress distribution and resistance of composite deck at Serviceability Limit State (SLS) and Ultimate Limit States (ULS) by leveraging results obtained from standard beam models typically used by structural designers or practitioners. A comprehensive parametric analysis using nonlinear finite element models is performed to validate the developed methodology. A comparison with the Eurocode 4 formulations highlights that the proposed method provides superior accuracy in estimating peak stress in concrete slabs under combined compression and bending. Additionally, it facilitates straightforward verification at the ULS in compliance with Eurocode requirements.

A unified CNN approach for guided wave-based damage detection, damage size estimation and reliability assessment demonstrated on a complex composite structure

Abstract This work presents a novel unified Convolutional Neural Network approach where broadband ultrasonic guided waves signals are processed in such a way that damage is first detected (binary classification) and then its severity assessed on continuous scale (multi-class classification) without resorting to different procedures. To test the learning approach and assess the classification procedures, a hyperparameter optimization is first carried out to determine the best data processing procedure. Then, the performance of the network is evaluated thoroughly. The results demonstrated the relationship between the model’s performance and SHM system parameters, including excitation signal, pre-processing approach and the number of paths utilized within a sparse distributed transducer network. Furthermore, the damage location is an important influence factor. In addition to that, ensemble voting is demonstrated to be the most accurate approach to achieve high reliability in damage detection and size assessment. The results show the capability of the proposed methodology (i) to detect early damage with highest possible accuracy (ii) to estimate the dimension of damage with limited error and reasonable accuracy, and (iii) to assess the reliability of the whole monitoring system through damage size estimation combined with a critical damage size approach.

Meso-scale investigation on the permeability of frozen soils with the lattice Boltzmann method

Complex composition and intricate pore-scale structure of frozen soils poses significant challenges in reliably and efficiently obtaining their permeability. In this study, we propose a modified quartet structure generation set (QSGS) numerical tool for generating frozen soils and present the development of a computational simulation code based on the multiple-relaxation-time lattice Boltzmann method (LBM). In the modified QSGS, the arc-shaped water-ice interface is depicted, and the influence of pore-scale geometry on freezing temperature is considered. The validity of combining the proposed QSGS model and the LBM code is proved by comparing calculated results to analytical and experimental results of porous media. Our objective was to investigate the effects of soil features, including porosity, grain diameter, shape anisotropy of soil particles, and ice content on the intrinsic permeability of frozen soil. Additionally, we examined the relationship between these features and the specific surface area and tortuosity. Numerical results show that the intrinsic permeability of frozen soils increases with increasing porosity, larger granular diameter, and anisotropy, which is identical with the pressure gradient. The presence of ice led to clogging flow pathways and drastically decreased the intrinsic permeability, which is significantly less than unfrozen soil with same effective porosity. This study provides a useful tool to investigate the intricate interplay between the pore-scale structure and the intrinsic permeability of frozen soils.

Efficient 3D temperature field prediction and visualization for AFP process analysis in composite structures

AbstractProcess modeling is an indispensable tool for establishing a foundation for process optimization and enhancing the quality of products. Analyzing the thermoforming process of polymer matrix composites presupposes the acquisition of accurate thermal histories. This paper introduces a numerical solution based on the finite difference (FD) model to predict the temperature field of angle‐ply composite structures during automated fiber placement (AFP) process along the machining trajectory. The proposed method incorporates three‐dimensional hybrid boundary conditions and considers temperature‐dependent material properties, significantly improving efficiency compared to the finite element (FE) model and overcoming challenges related to complex boundary conditions and anisotropic heat transfer in analytical solutions. Real‐time temperature measurement experiments were conducted to validate predictions, and further a specific temperature control strategy that meets process requirements was explored. In summary, the paper introduces a novel approach to address the temperature field in complex composite structure manufacturing processes.Highlights A novel 3D finite difference model predicts the temperature field in automated fiber placement. Improved prediction accuracy by managing complex boundary conditions. Effectively handled anisotropic heat transfer in complex composite structures. Superior efficiency over existing numerical methods. Developed a model‐based strategy for optimal temperature control within ±5°C.

Predicting damping in geometrically complex composite structures via increased interlaminar homogenisation

The widespread adoption of composite materials across the engineering sector requires that the vibration behaviour of these materials is more readily taken into account for new component designs. Current prediction methods are often prohibitively complex or expensive for such applications, or are restricted to very simple geometries. A simplified approach, shown previously by the authors to generate accurate and inexpensive predictions, is extended to geometrically more complex test cases in this work. The novel technique is shown to hold up well compared to the more established ‘layered’ approach, outperforming such predictions in some cases and highlighting the general applicability of the approach for damping predictions in general composite components.

Integrated MUSIC array for high-precision damage diagnosis in complex composite structures

Guided Wave (GW)-based Multiple Signal Classification (MUSIC) damage imaging presents several advantages, such as high resolution, which makes it a promising technique for localizing damage in composite structures. However, the application of this technology in aircraft is confronted with various challenges. The variability in performance of MUSIC array sensors is attributed to material and manufacturing process dispersion. Additionally, the conventional wiring of MUSIC array sensors adds considerable weight and is not compatible with complex structural configurations. Furthermore, within intricate configurations, the attenuation of scattering signals induced by structural damage impacts the accuracy of imaging. Moreover, the manual and individual placement of sensors on structures, along with structural anisotropy, may introduce phase errors in the signals detected by MUSIC array sensors. This can lead to a reduction in the accuracy of MUSIC imaging and result in compromised long-term sensor reliability. This paper proposes a high-precision integrated MUSIC array for the diagnosis of complex composite damage. This approach aims to address the challenges related to damage imaging in materials with complex structures. Impedance curve screening and surface-mount co-curing technology are utilized to manage the performance variation of MUSIC array sensors, enhance layout uniformity, and improve long-term stability. Subsequently, a focus compensation algorithm is proposed within the integrated MUSIC design to enhance precision, reduce weight, and adapt to complex structures. The effectiveness of the proposed method is confirmed through experimental validation on an actual complex composite wing box segment, demonstrating a maximum error of 2 cm in locating impact damage.

Printed PZT Transducers Network for the Structural Health Monitoring of Foreign Object Damage Composite Panel

The work presented here focuses on the structural health monitoring (SHM) of a foreign object damage (FOD) composite panel equipped with an innovative printed piezoelectric transducer network. The 3D woven composite FOD panel measures approximately 800 mm x 320 mm, is curved with a cross-sectional thickness varying from approximately 2 mm to 12 mm, and a stainless-steel leading edge is bonded at one of its sides. The core idea explored here is to rely on an innovative screen-printing technology to print a full piezoelectric transducer allowing to successfully achieve SHM on such a complex composite structure. This work is being carried out within the European project MORPHO – H2020. After printing a 25 elements PZT network, a four points bending fatigue experimental campaign using the PZT network along with other sensor technologies (embedded optical fibres with FBG sensors and acoustic emission sensors) is carried out. This unique experimental campaign allows to generate data and will help to develop diagnostic and prognostic methodologies for remaining life estimation and SHM of the FOD panel. It is demonstrated here through impedance measurements that the printing process associated with the printed PZT transducers is highly repeatable thus validating its use at a larger industrial scale. Furthermore, the printed piezoelectric transducers electromechanical behaviour is characterized and they are shown to be able to detect foreign object impact and to size and localize resulting damage using Lamb waves signals collected at different locations of the network. This innovative printing technology for PZT transducers network is thus extremely promising. It is furthermore highly advantageous to use the printed transducers for SHM instead of regular ceramic ones as this technology is non-intrusive, add negligible weight, can be printed during the manufacturing process, and arrays of transducers ensure easy availability of another transducer in case of failure of one.

Cross-Domain Feature Disentanglement for Interpretable Modeling of Tumor Microenvironment Impact on Drug Response.

High-throughput screening technology has enabled the generation of large-scale drug responses across hundreds of cancer cell lines. There remains a significant gap between in vitro cell lines and actual tumors in vivo in terms of their response to drug treatments yet. This is because tumors consist of a complex cellular composition and histopathology structure, known as the tumor microenvironment (TME), which greatly impacts the drug cytotoxicity against tumor cells. To date, no study has focused on modeling the impact of the TME on clinical drug response. In this study, we postulated that the intricate complexity of an actual tumor can be conceptually simplified into two separable components: cancerous cells and the tumor microenvironment. This assumption allowed us to model the influence of these two constituent parts on drug response through feature disentanglement. We employed a domain adaptation network to decouple and extract features from tumor transcriptional profiles. Specifically, two denoising autoencoders were separately used to extract features from cell lines (source domain) and tumors (target domain) for partial domain alignment and feature decoupling. The private encoder was enforced to extract information only about the TME. Moreover, to ensure generalizability to novel drugs, we employed a graph attention network to learn the latent representation of drugs, enabling us to linearly model the drug perturbation on cellular state in latent space. We validated our model on a benchmark dataset and demonstrated its superior performance in predicting clinical drug response and dissecting the influence of the TME on drug efficacy.

GW-MUSIC focusing algorithm with high accuracy for composite damage diagnosis

The Guided Wave (GW)-based Multiple Signal Classification (MUSIC) method for damage imaging in composite structures offers high-resolution and flexible sensor array placement, demonstrating considerable potential for precise damage localization. However, the application to real aircraft composite structures, characterized by complex structural features, presents substantial challenges. These include complex boundary reflections, pronounced anisotropy, and diminished scattered signals from damage sites. This paper presents a novel single-peak anisotropy-compensated MUSIC method for damage imaging of complex composites. This method, designed for improved localization accuracy, combines a self-excited calibration strategy to mitigate anisotropy with single wave peak focusing to amplify signals and reduce boundary reflections. Experimental validation on a complex composite wing structure demonstrated significant enhancements in localization precision, affirming the method's efficacy.

Study on Mechanical and Acoustic Emission Characteristics of Backfill-Rock Instability under Different Stress Conditions.

Unveiling the mechanical properties and damage mechanism of the complex composite structure, comprising backfill and surrounding rock, is crucial for ensuring the safe development of the downward-approach backfill mining method. This work conducts biaxial compression tests on backfill-rock under various loading conditions. The damage process is analyzed using DIC and acoustic emission (AE) techniques, while the distribution of AE events at different loading stages is explored. Additionally, the dominant failure forms of specimens are studied through multifractal analysis. The damage evolution law of backfill-rock combinations is elucidated. The results indicate that DIC and AE provide consistent descriptions of specimen damage, and the damage evolution of backfill-rock composite specimens varies notably under different loading conditions, offering valuable insights for engineering site safety protection.

Viscosity‐coupled curing simulation model for L‐shaped composites considering flow and compaction

AbstractComputational methods provide effective tools for the prediction of the curing behavior of composites, especially for large‐scale complex composite structures. The flow‐compaction analysis during curing receives insufficient attention in current studies. In this work, an integrated framework considering the heat transfer, the cross‐linking reaction, the flow and compaction, and the stress–strain module is established. A comprehensive resin flow termination criterion is proposed. L‐shaped composites with a stacking layer of [45/90/−45/0]s were fabricated, and the good agreement between the experimentally measured and the numerically predicted contour data validated the current model. Compared with Hubert's model, the prediction accuracy of the thickness and fiber volume content increases by 4.24% and 3.92%, respectively. Correlation analysis between the fiber volume ratio predicted by numerical methods and experimental results (94% for this method, −41% for Hubert's model) demonstrates the well‐capture of nonuniform fiber distribution in curved composites for the current strategy. The overall framework provides an accurate and promising tool for the prediction of the curing behavior of complex composite components.Highlights An integrated numerical methodology is proposed considering the resin flow and compaction. The viscosity‐temperature coupled feature of resin is considered in the permeability coefficients. A comprehensive resin flow termination criterion is proposed for an accurate prediction. The resin content, thickness, and spring‐in angle of L‐shaped laminates are characterized. The good correlation between the experimental and numerical results validates this method. Compared with the model proposed by Hubert, the prediction accuracy improves.

Three-dimensional heat transfer analysis of Hot Gas Torch (HGT)-assisted Automated Fiber Placement (AFP) for thermoplastic composites

Automated Fiber Placement (AFP) is a key additive manufacturing process for the production of complex composite structures. This study focuses on the in-situ AFP process for thermoplastic composites with Hot Gas Torch (HGT), which includes heating, consolidation, and solidification steps. Temperature control is critical to achieving high quality parts as it affects bond quality, crystallization, and solidification. However, previous studies have oversimplified convective heat transfer by assuming a constant coefficient, resulting in discrepancies between simulations and experiments. This paper introduces a novel distribution function to model the convective heat transfer coefficient, thereby improving temperature predictions. An optimization loop is used to determine the parameters of the function, which ensures agreement with experimental data. The proposed approach accurately predicts the temperature distribution, which is validated against unseen experimental results. By incorporating the distribution of the convective heat transfer coefficient, this study improves the understanding of heat transfer mechanisms in AFP for thermoplastic composites, leading to improved manufacturing processes and part quality.

Introducing hybrid deep neural networks to predict the transient vibrations of the sandwich systems reinforced by multi-phase nanocomposites

In advanced materials engineering, understanding transient vibrations in sandwich annular plates reinforced by multi-phase nanocomposites is essential. To adequately represent the geometry of these systems, a generic cylindrical coordination system is often used in modeling. The heterogeneous laminated plate is homogenized into an analogous uniform medium to facilitate analysis. In order to represent precise stress and strain distributions, taking into account rotating inertia and transverse shear deformation, a higher-order theory based on a hyperbolic function is used. The governing equations of motion are derived using Hamilton’s principle in polar coordinates, guaranteeing compliance with energy laws. The 2D generalized differential quadrature (GDQ) method effectively discretizes the governing equations for numerical solutions while retaining a high degree of accuracy in recording transient reactions. Furthermore, by solving the differential equations in the frequency domain, the Laplace transform approach sheds light on the dynamic behavior of the system. Using the deep neural networks (DNNs)-fuzzy approach improves the prediction of these composite structures’ vibrational activity. This hybrid approach uses fuzzy logic to control uncertainty and DNN for pattern identification. The DNN-fuzzy approach reliably predicts the intricate vibrational properties of multi-phase sandwich systems reinforced with nanocomposite using datasets from mathematical modeling. This combined use of mathematical techniques, theoretical models, and predictive algorithms improves our knowledge of complex composite structures’ transient vibrations and makes them more useful in a range of engineering domains.

Engineering digital light processing ceramic composites for wide-range flexible sensing arrays

Flexible ceramic composites are promising candidates in capacitive pressure sensing applications. However, the fabrication of complex composite structures typically involves costly and time-consuming processes such as lithography or mold utilization. Digital light processing (DLP)-based 3D printing offers a layer-by-layer approach via photopolymerization, facilitating rapid prototyping of various ceramic composite structures in a single-step synthesis process. This study presents the successful implementation of a flexible ceramic composite based on the highly dielectric ceramic BaTiO3 and conductive MWCNT fillers by employing DLP 3D printing to create an hourglass-shaped stress concentration structure, aiming at enhancing flexible capacitive sensing capabilities. Blending commercial flexible resin with 4-acryloyl morpholine monomers yields a photocurable resin formulation with appropriate mechanical flexibility, photocurability, and optimal suspension viscosity suitable for DLP 3D printing. Furthermore, the proposed 3D-printed sensor arrays comprising hourglass-shaped unit cells demonstrate improved linear sensitivity across a broad pressure range owing to efficient stress concentration effects in a symmetric geometry, as corroborated by both finite element methods and experiments. DLP 3D printing, combined with tailored resin formulations and optimized ceramic and conductive filler contents, enables the rapid prototyping of diverse sensor structures with significantly enhanced sensitivity, highlighting the versatility of this approach for a wide range of applications.

Biological and bio-inspired materials: Multi-scale modeling, artificial intelligence approaches, and experiments

Biological materials often possess remarkable properties and functionalities owing to their complex hierarchical and composite structures. Learning from nature can lead to revolutionary breakthroughs in materials science and innovative new technologies. This Special Issue titled “Biological and Bio-inspired Materials: Multi-scale Modeling and Artificial Intelligence Approaches” is a collection of research articles and comprehensive reviews utilizing multi-scale modeling, artificial intelligence approaches, and experiments to elucidate the characteristics of biological materials and design and optimize bio-inspired materials. The computational approaches of interest include but are not limited to molecular dynamics, lattice spring models, finite element analysis, genetic algorithms, neural networks, generative adversarial networks, and other modeling and artificial intelligence approaches for better understanding the structure-property relationships and underlying mechanisms of biological (natural) materials, and reproducing, designing, and optimizing bio-inspired materials. Novel experimental results, fabrication strategies, and applications of biological, bio-inspired and biomedical materials are also collected in this special issue.