A computational homogenization technique (CHT) based on the finite element method (FEM) is discussed to predict the effective elastic properties of honeycomb structures. The need of periodic boundary conditions (BCs) is revealed through the analysis for in-plane and out-of-plane shear moduli of models with different cell numbers. After applying periodic BCs on the representative volume element (RVE), comparison between the volume-average stress method and the boundary stress method is performed, and a new method based on the equality of strain energy to obtain all non-zero components of the stiffness tensor is proposed. Results of finite element (FE) analysis show that the volume-average stress and the boundary stress keep a consistency over different cell geometries and forms. The strain energy method obtains values that differ from those of the volume-average method for non-diagonal terms in the stiffness matrix. Analysis has been done on numerical results for thin-wall honeycombs and different geometries of angles between oblique and vertical walls. The inaccuracy of the volume-average method in terms of the strain energy is shown by numerical benchmarks

Natural plant fibers (NPFs) have emerged as a sustainable alternative in the manufacture of composites due to their renewability and low environmental impact. This has led to a significant increase in the use of natural plant fiber-reinforced polymers (NPFRPs) in a variety of industries. The diversity of NPF types brings a wide range of properties and functionalities to NPFRPs, which in turn highlights the urgent need to improve the properties of fiber materials in order to enhance their performance and suitability. This paper provides insight into the processing mechanisms behind NPF fiber treatments, exploring how these treatments affect the mechanical, thermal and environmental properties of NPFRPs. It also offers a critical assessment of the advantages and disadvantages of physical, chemical, biological and nanotechnological treatments. The findings of our analysis provide a basis for the development of future treatments that aim to enhance the material properties of NPFRPs, thereby increasing their competitiveness with conventional synthetic fiber-reinforced polymers. Finally, a novel thermoplastic resin composite system, Elium–NPFRP, is proposed that embodies the principles of green development. The system has been designed with the objective of capitalizing on the environmental benefits of NPFs while simultaneously addressing the challenges associated with the integration of NPFs into polymer matrices. The Elium–NPFRP composite system not only exemplifies the potential of NPFs for sustainable materials science, but is also a practical solution that can be implemented in a diverse range of applications, spanning automotive components to construction materials. This has the potential to reduce carbon footprints and promote a circular economy.

The governing equations for the buckling of honeycomb cores with various cell geometries under combined compression and shear are established and three types of core including rectangular, hexagonal and triangular cores are under consideration. After invoking the Bloch wave representation form, the equations are simplified by the periodicity and the hypothesis that the out-of-plane displacement remains zero at the intersections. Different cell geometries and load cases are taken into account and numerical results offer validation for the analytical solutions. Moreover, the results of Finite Element (FE) models show that the fine results can only be acquired by models with appropriate cell numbers. Experimental study is conducted on the regular hexagonal honeycomb structures. Both the results of the numerical benchmarks and the experiments prove the effectiveness of the proposed analytical method and the hypothesis for predicting the buckling load of honeycomb structures.

Experimental and numerical investigations are presented for a theory-guided machine learning (ML) model that combines the Hashin failure theory (HFT) and the classical lamination theory (CLT) to optimize and accelerate the design of composite laminates. A finite element simulation with the incorporation of the HFT and CLT were used to generate the training dataset. Instead of directly mapping the relationship between the ply angles of the laminate and its strength and stiffness, a multi-layer interconnected neural network (NN) system was built following the logical sequence of composite theories. With the forward prediction by the NN system and the inverse optimization by genetic algorithm (GA), a benchmark case of designing a composite tube subjected to the combined loads of bending and torsion was studied. The ML models successfully provided the optimal layup sequences and the required fiber modulus according to the preset design targets. Additionally, it shows that the machine learning models, with the guidance of composite theories, realize a faster optimization process and requires less training data than models with direct simple NNs. Such results imply the importance of domain knowledge in helping improve the ML applications in engineering problems.