Metamaterials are widely used for achieving tailored multi–physical properties. However, it is challenging to satisfy multiple property requirements simultaneously. For example, the mechanical and mass transport properties of bionic scaffolds have to be compromised. In this work, 3D–printed metamaterials with adjustable topological features are proposed, and a topological optimization strategy for coordinating multiple properties is developed. Tradeoffs between the mechanics, energy absorption capacity, and mass transport properties are accomplished under given a porosity. Anisotropic metamaterials with permeabilities of 2.34 ∼ 3.44 × 10-7 m2 and elastic moduli of 1.37 ∼ 3.14 GPa were obtained, and the gradient scaffolds were further designed to realize adjustable local characteristics. The optimized design technique described here can serve as an effective way to design more complex multilayered scaffolds with structural characteristics and biomechanical properties similar to those of natural tissue, so as to achieve an unprecedented level of tailoring multi–physics properties in tissue engineering, especially in the design of gradient scaffolds. Our study also represents advances in property decoupling, individual customization and collaborative design of metamaterials, which can be generalized to fluid and heat transport fields.

The current research aims to analyze the mechanical characterization of sandwich materials through a three-point flexural test. The sandwich structures in question composed of balsa wood as a core and four different types of fiber reinforced vinyl ester composite facesheets, namely Glass, Basalt, Glass/Carbon, and Basalt/Carbon. The sandwich panels were prepared using the vacuum infused processing method. The primary objective of this investigation is to explore the feasibility of utilizing basalt fibers for the production of structural parts in shipbuilding and replacing the already existed glass fibers. The flexural test was carried out with using three-point flexural test with varying the span length from 120 mm, 180 mm, 220 mm. Additionally, an analysis of variance (ANOVA) was then carried out to compare the mean values of properties deduced from these tests. The results showed that using basalt fibers instead of glass fiber reinforced enhanced the flexural stiffness of the sandwich structure. The flexural strength and modulus are shown to depend on span length and fiber type. The flexural modulus increases with an increase in span length. Similarly, flexural strength increases in glass fiber-based structures, while a slight reduction is observed in basalt fiber-reinforced structures the larger span length. The findings of this research suggest that basalt fibers hold potential as a replacement for glass fibers in producing structural components in shipbuilding. These results offer valuable information that can aid in the design and optimization of sandwich materials in shipbuilding.

Determining the allowable compression load (ACL) of notched composite laminates (NCL) requires consideration of buckling, intra- and inter-laminar damage, which is a costly and repetitive task due to the high dimensional and nonlinear relation of ACL to the numerous design variables. In this work, a high-throughput finite element analysis (FEA) and machine learning (ML) combined approach is proposed to predict ACL of laminates. First, the high-throughput FEA model of NCL covering all of the design variables is generated, and the corresponding critical compression loads in terms of buckling, intra- and inter-laminar damage are obtained. Then, ML models are trained, with inputs being the design variables of NCL and outputs being the critical compression loads. ACL and initial failure mode are determined by the minimum compression load of the three failure modes. Moreover, transfer learning is introduced to laminates with new design variables of new ply number, notch shape and laminate type. By fine-tuning the pre-trained ML model using scarce new data, the fine-tuned models are suitable for new laminates. Consequently, ACL and initial failure mode of notched laminates with various design variables are predicted.

The stochastic vibration analysis of composite laminated structures has been conducted extensively in the field of structural dynamics. Existing studies in this field are primarily conducted based on deterministic structural parameters, whereas the effects of parametric uncertainties on the stochastic vibration characteristics of composite laminated structures are disregarded. This study investigates a composite laminated rectangular plate by considering the effect of interval uncertainty in the intrinsic parameters and load on its stochastic vibration characteristics. A rapid analysis model for the structural stochastic vibration characteristics is established based on an improved kriging model. Additionally, an innovative approach that combines the improved kriging model with intelligent optimization is proposed to solve problems pertaining to uncertainty-propagation analysis of structures. Based on this method, an uncertainty-propagation analysis of structural stochastic vibration responses is efficiently implemented. The effectiveness of the proposed method is demonstrated by comparing the results with those obtained from Monte Carlo simulation. The numerical results indicate that different uncertain factors exert varying degrees of effect on the stochastic vibration characteristics of the plate. Finally, the effects of density, elastic modulus ratio, fiber orientation and load on the interval fluctuation patterns of uncertain responses are discussed.

This paper investigated the quasi-static and dynamic behavior of 3D auxetic metamaterial structures made from carbon fiber reinforced polymer (CFRP) laminated composite. The aim of the study was to enhance design methodologies for load-bearing and energy absorption applications of these 3D novel structures, filling the research gap in understanding their response to quasi-static and especially dynamic loadings. The two novel 3D structures were designed and fabricated by using an interlocking assembly method based on the 2D auxetic CFRP sheets, which were formed with hybrid double-arrow-head with re-entrant and star unit-cells and made with plain weave carbon epoxy prepregs. The finite element (FE) method was adopted to analyze the mechanical characteristics of the structures under the quasi-static and dynamic loading, and Hashin failure criteria were used to define damage in the structures. The study showed that the designed 3D auxetic CFRP structures simultaneously exhibit superior auxeticity, load-bearing, and energy absorption capacity.

The research involved analyzing a material solution for a 9 mm Full Metal Jacket (FMJ) Parabellum loaded with ballistic impact on a resin laminate strengthened with extra aramid layers. The goal of this research is to determine the impact of material (aramid/DCPD) layer thickness on the energy absorption performance of the laminate for blunt trauma as blunt criterion (BC). For this purpose, aramid fiber laminate with dicyclopentadiene (DCPD) resin matrix was prepared. Laminate samples were tested on the drop test and were subjected to ballistic loads. The ABAQUS/Explicit program and finite element method were used to conduct the study. Individual material systems' optimal solutions were created using numerical analysis, and they were then classified using the mass-efficiency criterion. The numerical results were compared with the experimental using samples prepared according to the modeling methods. Selected results were shown and discussed in the later part of the paper.

Due to the outstanding performance, composite materials are widely used and analyzing their properties and designing them based on performance has become a crucial task in the field of many manufacturing industries. Composite materials possess complex multiscale structures, and traditional fine-scale finite element modeling and analysis may lead to severe computational resource challenges. To overcome this difficulty, breakthroughs in key technologies of multiscale accelerated analysis algorithms are required. In this study, an innovative approach based on artificial intelligence and multiscale finite element method is presented. This approach involves partitioning the entire composite material structure into coarse grids that resemble homogenous structures of similar size, providing results consistent to fine-grid finite element analysis. By utilizing CNN for image feature recognition and employing the CGAN adversarial method, coarse-grid equivalent stiffness matrices and multiscale shape functions from completely random microstructures of composite materials can be obtained. Consequently, this enables a rapid response process from microstructure to low-resolution grid to high-resolution physical field, with remarkably accurate physical field results. Moreover, compared to traditional fine-grid finite element methods, this approach significantly reduces memory usage and computation time. This method is applicable to composite materials with varying shaped inclusions, different component properties, and diverse geometric distributions, allowing these materials to perform high-fidelity finite element calculations on coarse grids and predict their mechanical behavior. Furthermore, this breakthrough opens avenues for accelerating the optimization design of composite materials with diverse mechanical functionalities, by employing a bottom-up approach.

Carbon fiber reinforced polymer (CFRP) composites can be used to strengthen existing reinforced concrete (RC) structures. The CFRP laminate can be bonded to RC structure using epoxy adhesive via near-surface mounted (NSM) strengthening technique. However, existing literature generally lacks data about durability of NSM CFRP-to-concrete bond. In this study, strengthened concrete elements were exposed to laboratory-controlled environments (at approximately 20 °C/55 % RH, and water immersion at 20 °C) and natural field environments (to promote natural aging induced mainly by carbonation, high temperatures, freeze–thaw attack, and airborne chlorides) for up to four years. Durability tests were conducted yearly for the bond and its constituent materials. The highest bond strength degradations were nearly 12 % and 9 % for the specimens immersed in water and those exposed to freeze–thaw attack, respectively. Besides, environmental conversion factors of 0.88 and 0.93 were derived from a database of existing accelerated, and natural aging data from the present work, respectively.

Thin-walled edge cutting angle (TWECA) refers to a unique trait of honeycomb core materials in the course of cutting, which is also a main influencing factor for the surface quality of machining. To validly overcome machining defects, making accurate TWECA prediction during the cutting process is necessary. In this study, a TWECA model for various kinds of thin-walled edges was proposed. Firstly, the thin-walled edges composing honeycomb cores were classified and unified based on the structural features of the cores, which lays the foundation for the modeling of honeycomb core architecture as well as the TWECA prediction. Then, the geometric relationships between tool and various types of thin-walled edges during contact were analyzed, and the differences in TWECAs of thin-walled edges in different tool feed directions were explored. Finally, the TWECA model accuracy was validated by using the tool mark characteristics on the machined surface as the research target. As demonstrated by findings of this research, our model achieves accurate TWECA forecasting during cutting process, thus providing a theoretical foundation for the requirements of low-damage and high-quality machining.

Detecting near-edge damage in composite structural elements using guided wave-based techniques can be challenging, primarily due to the complexity of wave analysis arising from material anisotropy. Furthermore, scattered waves containing damage information can be contaminated by waves reflected from the edges, which makes detecting near-edge damage difficult to implement. In the literature, studies showed that in elastic materials, the edge of a structure can serve as a waveguide, enabling the existence of typical edge modes with concentrated energy at the edge. However, studies regarding edge waves in composite structures have received limited attention. This paper aims to explore the potential of detecting edge delamination damage in composite laminates using edge waves. The modal properties of edge waves in [(0/90)2]s composite laminates are investigated using the Semi-Analytical Finite Element (SAFE) method. Additionally, dispersion curves for quasi-isotropic composite laminates are calculated. Following this, numerical and experimental studies were conducted to investigate the sensitivity of edge waves in detecting edge delamination in the [(0/90)2]s composite laminates. The outcomes of this study offer physical insights into the modal properties of edge waves and confirm their effectiveness in detecting damage near the edges.