Kirigami Engineering: The Interplay Between Geometry and Mechanics

to mechanical metamaterials and related systems with particular emphasis on auxetic systems. Such materials show counter-intuitive, negative behaviours. For example, auxetic materials exhibit negative Poisson’s ratios which mean that these systems get fatter rather than thinner when stretched. Due to the superior properties exhibited by these systems, their study and characterisation has experienced a signifi cant increase, particularly in the last decade, something which may be perceived from the always increasing number of publications and patents in these areas. In fact, this issue is the seventh in a series of special issues published in physica status solidi (b) dedicated to auxetic materials and related systems (see [1–6]).

Auxetics and Other Systems of Anomalous Characteristics

Compressive properties and failure mechanisms of 3D-printed continuous carbon fiber-reinforced auxetic structures

The role of machine learning for insight into the material behavior of lattices: A surrogate model based on data from finite element simulation

Glass fiber (GF) Sheet Molding Compound (SMC) composites are popular lightweight materials due to their good processability. Hybrid SMCs expand the field of operation, combining the high stiffness of unidirectional carbon fibers (CF) with the economic efficiency of GF. Combinations of manufacturing deviations (delamination, varying GF content, CF misorientation) occur during the production of hybrid SMCs and impede the mechanical performance of the part. A function-oriented quality assurance instead of strict tolerances is proposed. Finite element (FE) simulations are computationally too expensive for an assessment within the cycle time. Hence, surrogate models are trained on multiple parameterized FE simulations. The surrogate models shall allow for an individual functional assessment in real-time based on integrated measurement inputs. This work focuses on the generation of parametrized FE simulations for measurement inputs and surrogate modeling. Simulations and surrogate models show acceptable deviations from tensile tests for multiple combinations of manufacturing deviations. The measurement uncertainty of the stiffness prediction is assessed for both the FE simulation and the surrogate models in accordance with the Guide to the Expression of Uncertainty in Measurement (GUM).

A predicting method for the mechanical property response of the marine riser based on the simulation and data-driven models

3D printed CoCrMo personalised load-bearing meta-scaffold for critical size tibial reconstruction

Axially loaded whole teeth of Atlantic wolffish exhibit negative Poisson's ratios due to their osteodentin microarchitecture.

Soft ionotronics are emerging materials as wearable sensors for monitoring physiological signals, sensing environmental hazards, and bridging the human-machine interface. However, the next generation of wearable sensors requires multiple sensing capabilities, mechanical toughness, and 3D printability. In this study, a metal-organic framework (MOF) and three-dimensional (3D) printing were integrated for the synthesis of a tough MOF-based ionogel (MIG) for colorimetric and mechanical sensing. The ink for 3D printing contained deep eutectic solvents (DESs), cellulose nanocrystals (CNCs), MOF crystals, and acrylamide. After printing, further photopolymerization resulted in a second covalently cross-linked poly(acrylamide) network and solidification of MIG. As a porphyrinic Zr-based MOF, MOF-525 served as a functional filler to provide sharp color changes when exposed to acidic compounds. Notably, MOF-525 crystals also provided another design space to tune the printability and mechanical strength of MIG. In addition, the printed MIG exhibited high stability in the air because of the low volatility of DESs. Thereafter, wearable auxetic materials comprising MIG with negative Poisson's ratios were prepared by 3D printing for the detection of mechanical deformation. The resulting auxetic sensor exhibited high sensitivity via the change in resistance upon mechanical deformation and a conformal contact with skins to monitor various human body movements. These results demonstrate a facile strategy for the construction of multifunctional sensors and the shaping of MOF-based composite materials.

The evolution of meta-biomaterials has opened up exciting new opportunities for mass personalisation of biomedical devices. This research paper details the development of a CoCrMo meta-biomaterial structure that facilitates personalised stiffness-matching while also exhibiting near-zero auxeticity. Using laser powder bed fusion, the porous architecture of the meta-biomaterial was characterised, showing potential for near-zero Poisson's ratio. The study also introduces a novel surrogate model that can predict the porosity (φ), yield strength (σy), elastic modulus (E), and negative Poisson's ratio (−υ) of the meta-biomaterial, which was achieved through prototype testing and numerical modelling. The model was then used to inform a multi-criteria desirability objective, revealing an optimum near-zero −υ of −0.037, with a targeted stiffness of 17.21 GPa. Parametric analysis of the meta-biomaterial showed that it exhibited −υ, φ, σy and E values ranging from −0.02 to −0.08, 73.63–81.38%, 41–64 MPa, and 9.46–20.6 GPa, respectively. In this study, a surrogate model was developed for the purpose of generating personalised scenarios for the production of bone scaffolds. By utilising this model, it was possible to achieve near-zero −υ and targeted stiffness personalisation. This breakthrough has significant implications for the field of bone tissue engineering and could pave the way for improved patient outcomes. The presented methodology is a powerful tool for the development of biomaterials and biomedical devices that can be 3D printed on demand for load-bearing tissue reconstruction. It has the potential to facilitate the creation of highly tailored and effective treatments for various conditions and injuries, ultimately enhancing patient outcomes.

In this work, continuous fiber reinforced thermoplastic negative Poisson's ratio structures (CFNPRSs) with rotating squares are fabricated by 3D printing based on a symmetrical orthogonal and one‐stoke path planning method. The influences of the printing path on the Poisson's ratio, elastic modulus and energy absorption of the structures under compression are systematically researched. The distribution of continuous fiber at the hinge has a great influence on the compression behavior of the structures. As the number of cross laps of continuous fibers at the hinge increases, the negative Poisson's ratio effect decreases while the elastic modulus and energy absorption increase. The printed CFNPRSs with no cross lap have the most obvious negative Poisson's ratio effect, with an average Poisson's ratio of −0.61. A comparative study on Poisson's ratio of the 3D printed polylactic acid negative Poisson's ratio structures (PANPRSs) is carried out, and the results show that the PANPRSs cannot achieve the negative Poisson's ratio effect because the insufficient stiffness of the printed rotation units violates the rigid assumption in theoretical model. Furthermore, the printed CFNPRSs with a lower relative density of 0.18 has an even better negative Poisson's ratio effect than the existing fiber‐reinforced auxetic structures fabricated by 3D printing. This work can provide a significant reference for the preparation of lightweight functional structures using 3D printing.Highlights The CFNPRSs with rotating squares are prepared by 3D printing technique. The path planning of the fiber at the hinge affects the properties of CFNPRSs. The negative Poisson's ratio of CFNPRSs is more obvious than that of PANPRSs. The relative density and Poisson's ratio of CFNPRSs have obvious advantages.

The surface accuracy of the large-aperture reflector antenna has a significant influence on the observation efficiency. Recent researchers have focused on using the finite element (FE) simulation to study the effect of gravity and heat on the deformation distribution of the main reflector. However, the temperature distribution of the antenna is challenging to obtain, and it takes a long time for the FE simulation to carry out FE modeling and post-processing. To address these limitations, this study presents a surrogate model based on Extreme Gradient Boosting (XGBoost) and deep Convolutional Neural Network (CNN) to get the deformation distribution of the main reflector quickly. In the design of the surrogate model, using the XGBoost algorithm and sparse sampling to solve the difficulty of obtaining the entire temperature distribution is first proposed, and then a deep CNN is developed for estimating deformation. Based on the effect of dynamic loads on the antenna structure, a diverse data set is generated to train and test the surrogate model. The results show that the surrogate model reduces the calculating time dramatically and can obtain the indistinguishable deformation compared to the FE simulation. This technique provides a valuable tool for temperature and deformation calculation of large-aperture antennas.

Hardness targeted design and modulation of food textures in the elastic-regime using 3D printing of closed-cell foams in point lattice systems

ABSTRACT: Some researchers have explored the feasibility of replicating the crack behavior of natural rocks with defects using three-dimensional (3D) printing technology. However, few have investigated changes in strain fields during compression experiments, even though this is crucial for understanding the crack behavior of 3D-printed rock-like specimens. This study examines the crack evolution and strain field distribution in the 3D-printed rock-like specimen to address this gap during the compression tests. In this study, a set of in-situ uniaxial compression tests was performed on 3D-printed rock-like specimens with an initial flaw with various inclined angles (α = 30°, 45°, 60°) using the high-resolution Micro-CT scanner with a loading apparatus. Based on the CT images of 3D-printed rock-like specimens obtained at different scanning steps, the 3D reconstruction and digital volume correlation (DVC) processes were applied to illustrate the damage evolution and quantify the 3D interior deformation of 3D-printed rock-like specimens. The results indicated that within the 3D-printed specimens, tensile cracks were first initiated near the internal flaw, followed by shear cracks or tensile-shear mixed cracks at the flaw tips. The characteristics of internal strain distribution before cracking indicated strain localization, and the cracking process of specimens was strongly correlated with radial strain distribution. 1. BACKGROUND Currently, methods for studying rocks' crack behavior, damage characteristics, and failure mechanisms in compression experiments include experimental approaches (such as rock coring and rock analog materials) and numerical simulation methods (such as finite element methods, discrete element methods, and finite difference methods). Due to their superior control and reproducibility, rock analog materials offer a better understanding of the crack behavior of rocks in compression tests. Traditional rock analog materials, including gypsum, sand, and cement-based materials, are prepared by casting methods. However, this method is low-precision, time-consuming, ineffective, and cannot produce samples with complex internal structures. 3D printing technology, one of the representative technologies of the Fourth Industrial Revolution, has been widely used in various fields, including rock mechanics and geotechnical engineering (Jiang et al., 2016; Song et al., 2018; Zhu et al., 2018). Researchers (Shao et al., 2023; Song et al., 2020; Zhou et al., 2020) have found through uniaxial and triaxial compression experiments that powder-based 3D-printed specimens exhibit lower strength, with their mechanical properties only matching those of low-strength sandstones, while resin-based 3D-printed specimens can match the high strength of natural rocks. Asadizadeh et al. (2019) studied the full-field strain evolution and crack propagation behavior of 3D-printed gypsum specimens with different initial defects in Brazilian split tests using Digital Image Correlation (DIC) technology based on powder-based 3D printers. They concluded that tensile cracking dominates fracturing under loading, while shear cracking is challenging. Song et al. (2023a) explored the mechanical response and deformation-cracking behavior of 3D-printed sand specimens with prefabricated flaws, shedding light on how fracture characteristics impact strength and failure modes. The results indicate that the fracture mode of 3D-printed sand specimens strongly depends on the inclination angle of the initial defect.

Architected material analogs for shape memory alloys