Optimization Of Composite Structures Research Articles

Study on design optimization of GFRP tubular column composite structure based on machine learning method.

Circular reinforced concrete wound glass fiber reinforced polymer (GFRP) columns and reinforced concrete filled GFRP columns are extensively utilized in civil engineering practice. Various factors influence the performance of these two types of GFRP columns, thereby impacting the whole project. Therefore, it is highly significant to establish the prediction models for ultimate displacement and ultimate bearing capacity to optimize the design of the two types of GFRP columns. In this study, based on the experiments conducted under different conditions on the two kinds of GFRP columns, automatic machine learning along with four other commonly used machine learning methods were employed for modeling to analyze how the column parameters (cross section shape, concrete strength, height of GFRP column, wound GFRP wall thickness, inner diameter of wound GFRP column) affect their performance. The differences in performance among these five machine learning methods were analyzed after modeling. Subsequently, we obtained the variation patterns in ultimate displacement and ultimate bearing capacity of the columns influenced by each parameter by testing the data using the optimal model. Based on these findings, the optimal design schemes for the two types of GFRP columns are proposed. The contribution of this paper is three-fold. First, AutoML sheds light on the automatic prediction of ultimate displacement and ultimate bearing capacity of GFRP column. Second, in this paper, two optimal design schemes of GFRP columns are proposed. Third, for AEC industrial practitioners, the whole process is automatic, accurate and less reliant on data expertise and the optimization design scheme proposed in the article is relatively scientific.

A machine learning-enabled prediction of damage properties for fiber-reinforced polymer composites under out-of-plane loading

For understanding the damage morphology in fiber-reinforced plastics under out-of-plane loading, engineers/ and designers mostly rely on experimental investigations and numerical models, which could be costly in terms of resources and computational requirements. Thus, pragmatic predictive approaches that can offer ready-to-use tools for material optimization have gained traction in recent years. This paper proposes a machine learning model, developed with the use of previous experiments, to predict the damage behaviour of E-glass fiber-reinforced plastics in sandwich structures and laminates. The focus is on quasi-static indentation with different indenter geometries, using acoustic signals for damage monitoring. A total of 10 optimal-performing regression algorithms were employed to develop the prediction models. The developed models based on total energy gave acceptable results for all specimens (R-squared value: 0.9932 – 0.9999), while prediction models using force produced less accurate results (R-squared value: 0.8693) for a sandwich structure subjected to a conical indenter. Results showed that energy absorption of composites provided the most reliable data for the development of predictive models. The load profiles could also be used when the contact area is limited (e.g., for hemispherical and conical indenters). The best-performing model for higher surface areas of the indenter increased was k-NN, thanks to its ability to capture complex and nonlinear relationships between the input features and the target variable. Overall, the developed model could provide more autonomy and control for the material optimisation of composite structures for industrial applications with reduced time and cost constraints.

Theoretical modeling and vibration analysis of composite laminated wing-box structures of hydrogen-electric aircraft under hygrothermal environment

For the sake of achieving the goal of ultra-low or even zero carbon emissions during flight, hydrogen-electric aircraft has been receiving increasing emphasis. Nevertheless, the hydrogen-electric aircraft owns an all-composite fuselage configuration and is subjected hygrothermal environment, which have a significant effect on the aircraft dynamics, especially for wing structure. In this study, an improved orthogonal polynomial solution based on the Rayleigh-Ritz method is developed for the dynamic analysis of composite laminated wing-box structures (CLWBSs) of hydrogen-electric aircraft with humid and hot environment and elastic boundary. In this solution approach, the potential energy, kinetic energy, elastic potential energy and hygrothermal potential energy of CLWBSs are derived from the framework of Kirchhoff thin plate hypothesis. A set of artificial springs is imported to the free edges of CLWBSs to simulate the elastic boundary and connection between two plates. The convergence, accuracy and computational efficiency of the proposed formulation are validated by experiment and finite element analysis. The effect of the coupling parameters and the elastic boundaries due to flexible composite fuselage in hygrothermal environment created by the hydrogen fuel cells are systematically investigated which have been confirmed by theoretical research, finite element analysis and experiment. The related findings indicate that the present approach provided an effective formulation for the vibration analysis and optimization of flexible composite wing-box structures. It can be applied in the field of aerospace and navigation industry at the same time.

Analysis of load-bearing characteristics of bi-modulus composite stiffened plates: Numerical simulation method and large-scale experimental verification

A large number of experiments show that composite materials usually show different modulus properties in tension and compression (bi-modulus). However, for actual composite engineering structures, especially complex models, the numerical simulation calculation is usually based on a single modulus. In order to consider the different modulus characteristics of composite materials in tension and compression, researchers put forward simplified constitutive models of dual-modulus materials. However, these constitutive models have not been verified by a large number of experiments, especially by complex models. In this paper, the GWFMM model is simplified and used to analyze complex structures with different modulus in tension and compression. The experimental results of stiffened plate show that the improved GWFMM model can be used to analyze different modulus of tension and compression of complex stiffened structures. In addition, the influence of elastic modulus ratio in tension and compression under different experimental load conditions is further explored, and the influence law of bending and torsion on structures with bi-modulus is discussed. The relevant conclusions provide reference for the design and optimization of full-scale composite structures in marine engineering.

Flexible composite structure with customizable in-plane Poisson’s ratio under large deformation

Regulating the inherent Poisson's ratio of soft elastic materials under large deformation is important for their application in emerging fields, such as morphing structures and stretchable electronics. In this study, we selected a composite in which an auxetic honeycomb was embedded in a soft material to regulate the in-plane Poisson's ratio. Based on a cantilever beam model, a theoretical model was established to guide the design optimization of composite structures. The macroscopic constitutive relation and in-plane Poisson's ratio of the composite structures with different configurations were predicted by the theoretical model, which were consistent with the finite element analysis results. In the 30% strain range, the slope of the Poisson's ratio–strain curve decreases with an increase in the length of the inclined bar, and the in-plane Poisson's ratio increases with an increase in the angle between the transverse bar and the inclined bar. In addition, we propose an optimization design method with the in-plane Poisson's ratio as the objective function and then optimize and fabricate a skin structure with an in-plane Poisson's ratio of approximately zero for the morphing wings. The achievements of this study provide theoretical guidance for regulating the in-plane Poisson's ratio of thin flexible layers.

Multi-scale design of composite material structures for maximizing fundamental natural frequency

This study deals with the topology optimization (TO) of linear elastic composite structures considering fundamental natural frequency maximization. The concept of functionally graded structures (FGS) is used for anisotropic composite material structure design to improve the structural dynamic performance. The rational approximation of material properties (RAMP) method was employed as an alternative to the solid isotropic material with penalization (SIMP) method to improve the convergence stability for eigen-frequency problem. In the SIMP method, the occurrence of local eigen-modes at low frequencies in the void region causes ill-convergence, whereas the RAMP method prevents local eigen-modes from occurring. The method of moving asymptotes is used as an optimizer. Numerical verification was performed for three structural types: cantilever beam, double-clamped beam, and four-pinned beam structure. In addition, for cantilever beam design, bi-objective optimization for maximizing fundamental natural frequency as well as minimizing structural compliance was performed using the adaptive weighting method that quantitatively reflects the designer's preference by adjusting the weight for each objective function at each iteration.

Multi-objective optimization of composite sandwich structures using Artificial Neural Networks and Genetic Algorithm

Sandwich structures offer significant opportunities to improve the performance of many industrial applications such as aerospace, automotive, and marine. The design of a composite sandwich structure is often challenging because it is driven by the balance between the weight and cost of these structures. In this paper, a multi-objective optimization model using a Genetic Algorithm (GA) and an Artificial Neural Network (ANN) is presented to evaluate a new optimization approach in terms of weight and cost minimization for the composite sandwich structures. Classical lamination and beam bending theories were used, along with Monte Carlo simulation, to generate the design data for the proposed composite sandwich structure, which is applied as a floor panel for a high-speed train. Multilayer feedforward neural networks were used for predicting safety factors, cost, and weight of the designed structure based on the following inputs: core density, core thickness, face sheet materials’ combinations, and applied load. The trained Neural Network model was able to predict the considered results with a good performance metric, namely the coefficient of determination (R2 = 0.99) and the Mean Square Error (MSE = 1.3·10−5).Multi-objective optimization for cost and weight minimization was performed with a Genetic Algorithm using the derived ANN model. The obtained Pareto front provided several non-dominated optimal points leading to insights on the optimization process. The Finite Element Method (FEM) was used to model the key points of the optimal designs (i.e. the design with the lowest cost, weight, and Pareto optimal points). The FEM and optimization results had a maximum deviation of about 8.9%, indicating a good agreement between the two techniques.The newly elaborated methodology demonstrates a new approach for obtaining the optimum design of the investigated composite sandwich structure constructed from honeycomb core and laminated face sheets in terms the cost and weight. The study concluded that the use of Carbon Fiber-Reinforced Plastic (CFRP) or Fiber-Metal Laminate (FML) face sheets results in significant weight savings of about 59.5% and 48.6%, respectively, compared to an all-aluminum sandwich structure.

Buckling optimization of curvilinear fiber-reinforced composite structures using a parametric level set method

Buckling optimization of curvilinear fiber-reinforced composite structures using a parametric level set method

Multi-Scale Design and Optimization of Composite Material Structure for Heavy-Duty Truck Protection Device

Multi-Scale Design and Optimization of Composite Material Structure for Heavy-Duty Truck Protection Device

Memeplex-based memetic algorithm for the multi-objective optimal design of composite structures

Hybrid construction is proposed to decrease costs in lightweight structures using fiber-reinforced plastics (FRP) composite materials. Minimum weight, minimum strain energy (stiffness), and minimum energy variability of the structural system response are the objectives of the robust design optimization approach applied to composite shell structures with stiffeners. The trade-off depends on given stress, displacement, and buckling constraints imposed on composite structures considering non-linear geometric behavior. The design variables are ply angles and ply thicknesses of shell laminates, the cross-section dimensions of stiffeners, and the variables related to the selection of materials and their distribution at the laminate level and subsequently of the laminates along the structure. A Multi-Objective Memetic Algorithm (MOMA) applies multiple learning procedures exploring the synergy of different cultural transmission rules. An enlarged virtual population with a dual age-dominance nature captures and updates the Pareto curve. The concept of memeplex controls the meme selection and their propagation along the hybrid genetic and cultural evolution path. The success of memetic learning procedures is analyzed by measuring each meme's relative and absolute success events according to different approaches, depending on the reference time used in evolutionary history. Results show that MOMA is promising in multi-objective optimization of composite hybrid structures.

Application research in multi-objective constrained optimization of composite wing structure

In order to explore the optimization of composite wing structure, this paper reviews the past research works of optimization of composite wing structures worldwide and in China. It concludes that most optimization methods and calculations are useful but only applied with specified theoretic model. Then relevant composite optimization theories and strength constraints with composite lamination failure criteria, stability, aeroelasticity are sorts out. Optimizing algorithm and applied solution rounding strategy are also listed. Concentrating on weight-reduction analysis on the thickness, sequence, and angle of the composite stacking under multiple constraints, a practical model of a composite wing structure example with a large aspect ratio is completed, and the calculation is based on the finite element analysis tool Nastran and the genetic algorithm Matlab program. Two optimal solutions are given with detailed differences and the comparison of the composite structure parameters before and after calculation is also recorded. The application example indicates the feasibility of the optimization method and quadratic rounding process, the detail structure performance improvements with skin lamination change under the calculation are illustrated at the same time.

Additive Manufacturing and Characterization of Sustainable Wood Fiber-Reinforced Green Composites

Enhancing mechanical properties of environmentally friendly and renewable polymers by the introduction of natural fibers not only paves the way for developing sustainable composites but also enables new opportunities in advanced additive manufacturing (AM). In this paper, wood fibers, as a versatile renewable resource of cellulose, are integrated within bio-based polylactic acid (PLA) polymer for the development and 3D printing of sustainable and recycle green composites using fused deposition modeling (FDM) technology. The 3D-printed composites are comprehensively characterized to understand critical materials properties, including density, porosity, microstructures, tensile modulus, and ultimate strength. Non-contact digital image correlation (DIC) technology is employed to understand local stress and strain concentration during mechanical testing. The validated FDB-based AM process is employed to print honeycombs, woven bowls, and frame bins to demonstrate the manufacturing capability. The performance of 3D-printed honeycombs is tested under compressive loads with DIC to fully evaluate the mechanical performance and failure mechanism of ultra-light honeycomb structures. The research outcomes can be used to guide the design and optimization of AM-processed composite structures in a broad range of engineering applications.

Structural topology optimization of three-dimensional multi-material composite structures with finite deformation

In this work, an explicit three-dimensional (3D) topology optimization approach is presented for multi-material composite structures accounting the finite deformation effect. The proposed method employs different sets of 3D Moving Morphable Voids (MMVs) to identify each phase material, resulting in explicit geometric descriptions of the optimized composite structures and a reduction in the number of design variables. The decoupling between the topology description and finite element analysis of composite structures enables the removal of redundant degrees of freedom, thereby mitigating the convergence issue of finite deformation analysis caused by low-density elements and leading to significant computational savings. Numerical experiments of composite structures with two- and three-phase materials are optimized to validate the accuracy and efficiency of the proposed method. The results demonstrate that, under finite deformation, the distribution of each phase material in optimized 3D composite structures is significantly affected by the amplitude of external loads, and the optimized layout could be quite different from its counterpart under small deformation assumption.

Shakedown-reliability based fatigue strength prediction of parts fabricated by directed energy deposition considering the microstructural inhomogeneities

For mechanical components made by additive manufacturing (AM) techniques such as directed energy deposition (DED), micropores and other defects prevalently exist in the microstructure and they significantly reduce the reliability of these parts. To allow the influence of these microstructural features over the load bearing capacity to be considered in the design stage of AM structures, in the present paper we developed a multiscale numerical approach. The goal of this approach is to predict the failure probability of AM structures subjected to time-varied loadings, and it is realized through combining statistical homogenization, shakedown analyses, and reliability methods such as first-order reliability method and Monte Carlo simulation. Based on this strategy, we employed statistics of material parameters obtained from micromechanical models as inputs, implemented numerical tools and applied them to two exemplary structures, the plate with a hole and an aircraft bracket. Through a series of case studies carried out using different methods and under different assumptions of material randomness, the paper confirmed the robustness of the derived results and explained the mechanism of how micropores influence the structural reliability. The method developed in this paper can be a viable means for the design and optimization of metallic and metal matrix composite structures produced by AM techniques.

Strength design optimization of sandwich composite structures under heavy dynamic loads

Recent researches have witnessed an increased interest in the Rapid Runway Repair (RRR) methods to rehabilitate damages that may be caused by different incidents, such as: natural disasters of earthquakes, floods or man-made vandalism in civil wars. RRR is a strategic process for airport operations for civil and peace-making missions. The current RRR techniques like precast concrete slabs, metal mats, and fiberglass mats have different pros and cons. The current study numerically investigates CFRP sandwich composite structure for RRR usage, where its strength is maximized by design optimization to reach the possible carrying aircraft wheel capacity and safety factors. The proposed composite structure is advantageous of no corrosion, low erection time, high capacity-to-weight ratio, same finish of runway surface and repaired area, and can be applied over spots of unlevelled or inadequate bearing capacity of 60 cm diameter. The strength of the basic design of composite sandwich structure is first assessed to its maximum allowed carrying aircraft wheel capacity by FE modelling. Secondly, The Genetic Algorithm (GA) optimization technique is applied for maximizing the strength of the composite structure webs satisfying the minimum safety factor of five failure criteria of Tsai-Wu, Tsai-hill, Hoffman, Hashin and maximum stress. Finally, the achieved results promoted the usage of the composite structure to operate at the taxiways, runways, and theoretically, landing and take-off areas.

Finite element solutions for bending analysis of simply supported two layered un-symmetric cross-ply (0/90) square plate under uniformly distributed transverse load

Laminated composite plates are extensively used in aerospace, military, civil infrastructure, and automotive industries due to their benefits, including superior fatigue resistance, high strength, and lightweight properties. Customizable fiber orientation, materials, and stacking patterns are additional advantages. Therefore, conducting rigorous analyses to evaluate deflections and stress levels in these plates is essential for maintaining structural integrity and optimal performance across various applications. The solutions presented in this study rely on the utilization of a higher-order shear deformation theory (HSDT), which offers a notably precise depiction of the plate's behavior. The theoretical model employed in this research accounts for deformations and the variation of stresses along the thickness direction. Finite element formulations are employed to derive the solutions. In order to ascertain the accuracy and reliability of the current formulations and solution methodologies, the obtained results are systematically compared with those documented in the existing literature. This rigorous comparison serves as a robust validation of the approach applied in this study. Once the accuracy of the solutions is established, numerical results for two layered cross-ply composites with real material properties are presented. These results are in good agreement with the findings reported in the literature. By incorporating Higher Order Shear Deformation Theory (HSDT), the accuracy of predictions is significantly enhanced. This improvement ensures a more dependable foundation for the design and optimization of composite structures.

Multi-Objective Optimization of Thin-Walled Composite Axisymmetric Structures Using Neural Surrogate Models and Genetic Algorithms.

Composite shells find diverse applications across industries due to their high strength-to-weight ratio and tailored properties. Optimizing parameters such as matrix-reinforcement ratio and orientation of the reinforcement is crucial for achieving the desired performance metrics. Stochastic optimization, specifically genetic algorithms, offer solutions, yet their computational intensity hinders widespread use. Surrogate models, employing neural networks, emerge as efficient alternatives by approximating objective functions and bypassing costly computations. This study investigates surrogate models in multi-objective optimization of composite shells. It incorporates deep neural networks to approximate relationships between input parameters and key metrics, enabling exploration of design possibilities. Incorporating mode shape identification enhances accuracy, especially in multi-criteria optimization. Employing network ensembles strengthens reliability by mitigating model weaknesses. Efficiency analysis assesses required computations, managing the trade-off between cost and accuracy. Considering complex input parameters and comparing against the Monte Carlo approach further demonstrates the methodology's efficacy. This work showcases the successful integration of network ensembles employed as surrogate models and mode shape identification, enhancing multi-objective optimization in engineering applications. The approach's efficiency in handling intricate designs and enhancing accuracy has broad implications for optimization methodologies.

An iso-contour method for automated fiber placement optimization of composite structures

A new method for numerical optimization of fiber-steered composites is presented, which allows to control efficiently and effectively the curvature of the fibers of single- or multi-layer composite structures. It is based on the introduction of an artificial surface defined and controlled by a relatively small number of control points, which is optimized to identify optimal fiber orientations varying smoothly over the panels. Curvature constraints like the maximum fiber curvature constraint, MFCC, or the average fiber curvature constraint, AFCC, are respected explicitly by the method to ensure manufacturability of the composite component. Three validation cases are regarded where results of the unconstrained case are compared to those of established methods to illustrate the validity of the new approach. They are complemented by results considering curvature constraints showing that optimal structures depend strongly on the chosen curvature thresholds. Finally, a rib optimization of a wingbox structure is realized as a more complex case.

CO2 capture in activated pyrolytic coke/metal oxide nanoparticle composites

Carbon dioxide (CO2) capture from flue gas is of utmost importance in mitigating greenhouse gas emissions. This study focuses on enhancing the CO2 adsorption capacity of activated carbon (AC) composites through the incorporation of metal oxides. AC, AC/Fe3O4, and AC/MgO composites were synthesized and characterized using various analytical techniques including Raman and FTIR spectroscopy, TGA, SEM, EDX, XRD, AFM, BET, and VSM analysis. The CO2 adsorption isotherms of the AC/MgO and AC/Fe3O4 composites were measured at different temperatures and pressures using quartz crystal microbalance (QCM). The Langmuir model was successfully employed to fit the experimental data, with AC/MgO and AC/Fe3O4 exhibiting adsorption capacities of 38.567 mmol g− 1 and 71.963 mmol g− 1, respectively, at 298.15 K and 5 bar. These results demonstrate the significant enhancement of CO2 adsorption capacity achieved by incorporating metal oxides into the AC composite structure. Furthermore, the regeneration efficiency of the adsorbents was evaluated through multiple adsorption/desorption cycles, revealing that AC, AC/MgO, and AC/Fe3O4 maintained adsorption capacities of 94.1%, 97.9%, and 96.5%, respectively, after six consecutive cycles. This confirms their stability and reusability under practical conditions. These findings contribute to our understanding of CO2 capture using activated pyrolytic coke and metal oxide nanoparticle composites, highlighting the promising potential of AC/MgO composites as effective adsorbents for CO2 capture applications. Further investigation and optimization of composite structures and synthesis methods are warranted to enhance their overall performance and applicability

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

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