Nacre-like structures exhibit excellent mechanical properties under low-velocity impact, but the effectiveness of the nacre-like designs under high-velocity impact remains unclear. In this study, the process of a spherical projectile impacting on a nacre-like plate over a wide range of velocities is simulated using the finite element method. A three-dimensional finite element model is constructed and validated against the test data of the target perforation in terms of residual velocity and fracture morphology. The effects of impact velocity, interface strengths, and geometric sizes on the impact resistance capabilities are systematically investigated, and a dimensionless geometrical parameter is proposed to reveal the mechanism affecting the fracture toughness of nacre-like materials. It is found that the impact resistance of the nacre-like material gradually weakens with impact velocity increasing and is inferior to that of homogeneous plates under high-velocity impact. Moreover, the fracture toughness of nacre-like materials depends on the competition mechanism between interfacial enhancement and strength weakening at different impact velocities. These findings provide significant guidance on applying bio-inspired structures to design protective materials.

Advancements in dynamic modeling methods of robotic manipulator are critical to the effective implementation of model-based control. Traditional approaches rely on rigorous first-principles-based dynamic modeling and precise parameter identification, while this paper explores an alternative through data-driven model reconstruction. To tackle the curse of dimensionality in the model reconstruction of a serial robotic manipulator with multi-degree-of-freedom, a relative activation indicator is proposed. Based on this indicator, the k-means clustering algorithm is utilized to classify the data under different working conditions. Subsequently, we leverage the fundamental prior knowledge to find the dynamical characteristics of each cluster and reconstruct the dynamic model in a stepwise manner using the method of sparse identification of nonlinear dynamics (SINDy). For the library generation of SINDy, the strategy of double-feature-set for serial manipulators with common joint types is proposed. Simulation results show that the stepwise model reconstruction approach not only reduces the size of the library of candidate functions but also decreases the impact of data noise on the reconstruction results. Finally, controllers based on the reconstructed models are deployed on the experimental platform and the experimental results demonstrate the improvement in trajectory tracking performance and the potential of the proposed method in engineering applications.

Non-Schmid (NS) effects in body-centered cubic (BCC) single-phase metals have received special attention in recent years. However, a deep understanding of these effects in the BCC phase of dual-phase (DP) steels has not yet been reached. This study explores the NS effects in ferrite-martensite DP steels, where the ferrite phase has a BCC crystallographic structure and exhibits NS effects. The influences of NS stress components on the mechanical response of DP steels are studied, including stress/strain partitioning, plastic flow, and yield surface. To this end, the mechanical behavior of the two phases is described by dislocation density-based crystal plasticity constitutive models, with the NS effect only incorporated into the ferrite phase modeling. The NS stress contribution is revealed for two types of microstructures commonly observed in DP steels: equiaxed phases with random grain orientations, and elongated phases with preferred grain orientations. Our results show that, in the case of a microstructure with equiaxed phases, the normal NS stress components play significant roles in tension-compression asymmetry. By contrast, in microstructures with elongated phases, a combined influence of crystallographic texture and NS effect is evident. These findings advance our knowledge of the intricate interplay between microstructural features and NS effects and help to elucidate the mechanisms underlying anisotropic-asymmetric plastic behavior of DP steels.

The present study proposes a modified random sequential absorption (RSA) algorithm to generate a representative volume element (RVE) model for predicting the elastic properties of discontinuous curved fiber reinforced composites (DCFRCs) with varying fiber waviness functions and orientations. A small-move method was proposed to modify the traditional RSA algorithm. In comparison with the original RSA algorithm, the generation efficiency of the proposed modified RSA algorithm increased by over 40%, and the achievable maximum fiber volume fraction could reach up to 15% with a fiber aspect ratio of 15. The generated RVE model was utilized in conducting finite element analysis to investigate the effect of fiber waviness and wavy functions on the elastic properties of DCFRCs. Finally, a modified rule-of-mixture was proposed to predict the elastic properties of DCFRCs with various fiber orientations. The results indicated that the elastic properties predicted by the modified rule-of-mixture were in good agreement with those obtained from the RVE model, thereby demonstrating its effectiveness.

Osteoarthritis is one of the most common joint diseases, leading to joint pain, dysfunction, and a reduced quality of life for patients. Therefore, it is particularly important to explore more effective prevention, treatment and management methods to relieve patients’ pain and enhance their quality of life. Among physical therapies, pulsed electrical stimulation (PES) is considered to be a promising treatment method due to its high safety and ease-of-use features. PES provides a non-invasive, safe and effective option for patients. However, there are fewer studies on the biomechanical changes of PES in periarticular tissues, and its effects on the biological behavior of chondrocytes remain unknown. This study investigated the effects of PES on the biomechanical properties of osteoarthritic joints and the biological behavior of chondrocytes. The results showed that PES with an intensity of 10 mA and a frequency of 4 Hz increased the cross-sectional area of muscle fibers, prevented muscle atrophy and loss of function, and restored the mechanical properties of muscle tissue. PES also effectively increases the resistivity of knee osteoarthritis cartilage tissue, as well as the elastic modulus of cartilage, which can enhance the biomechanical characteristics of cartilage tissue. PES also promoted the metabolic activity of chondrocytes and increased cartilage matrix synthesis, thereby improving the overall structure and mechanical properties of cartilage tissue. Additionally, cellular experiments showed that 5 consecutive days of 800 mV PES significantly increased the expression level of Piezo1 gene in chondrocytes. At the same time, the expression of type II collagen and transforming growth factor beta increased, while the expression of matrix metallopeptidase 13 decreased. These changes favored the promotion of cartilage matrix synthesis. This has a positive effect on protecting and improving joint health and reducing the impact of osteoarthritis, and is important for understanding the mechanism of action of PES on chondrocytes and the development of related therapeutic strategies.

Dielectric elastomer (DE) is an electroactive polymer with the characteristics of high energy output, great flexibility, lightweight, mechanical compliance, and low cost, which are particularly suitable for DE energy generators. Energy harvesting efficiency is a key index to evaluate the performance of the energy generator, which depends on the structural configuration and the mechanical and dielectric properties of the DE material. This paper proposes a fractional viscoelastic polarization(FVP) model by combining the fractional viscoelasticity model and the polarization-based lumped parameter model. A dynamical model of a cone dielectric energy generator (CDEG) considering stretch-dependent electrostriction and nonlinear viscoelasticity is established. Additionally, a deep neural network (DNN) model is developed to explore the relationships between various parameters and the output energy of CDEGs to efficiently and accurately predict the energy output of CDEGs. Based on the DNN model, optimal parameter designs for CDEGs are obtained by using non-dominated sorting genetic algorithm II (NSGA-II). The experiments verified that the FVP model predicts accurately the output energy of CDEG and the established optimal design framework can accurately provide the optimal design parameters of CDEG, which offers deep insights for the design and fabrication of a high-efficiency dielectric energy generator.

Ceramic matrix composites have broad application prospects in the aerospace field due to their high temperature resistance and oxidation resistance. The effect of temperature and environment atmosphere on the fracture toughness and failure mechanisms of two-dimensional plain-woven SiC_f/SiC composites was investigated. The results show that they exhibit pseudo-plastic deformation behavior at different temperatures. The fracture toughness is as high as 48 MPa m^1/2 at room temperature, and gradually decreases with rising temperature. The difference in fracture toughness between argon and air initially increases and then decreases with rising temperature. Furthermore, the high-temperature failure mechanisms of these composites were analyzed through macro and micro analysis. Based on this, a physic-based temperature-dependent fracture toughness model considering matrix toughness, plastic power, fiber pull-out, and residual thermal stress was developed for fiber-reinforced ceramic matrix composites. The model has been well validated by experimental results. An analysis of influencing factors regarding the evolution of fracture toughness was conducted by the proposed model. This work contributes to a better understanding of the mechanical performance evolution and failure mechanisms of ceramic matrix composites under multifield coupling conditions, thereby promoting their applications.

Auxetic metamaterials have attracted much attention due to their outstanding advantages over traditional materials in terms of shear capacity, fracture resistance, and energy absorption. However, there are lack of design inspirations for novel auxetic structures. According to the materials databases of atomic lattice, some natural crystals possess negative Poisson’s ratio (NPR). In this paper, the mechanism of auxeticity in microscale Ti crystal is investigated through density functional theory simulation. Then we propose a macroscopic auxetic metamaterial by mimicking the microscopic atomic lattice structure of the bodycentered cubic Ti crystal. The NPR property of the macroscopic metamaterial is verified by theoretical, numerical and experimental methods. The auxeticity keeps effective when scaling up to macroscopic Ti crystal-mimic structure, with the similar deformation mechanism. Furthermore, from the geometric parameter investigation, the geometric parameters have great influence on the Poisson’s ratio and Young’s modulus of the macroscopic metamaterial. Importantly, an optimized structure is obtained, which exhibits 2 times enhancement in auxeticity and 25 times enhancement in normalized Young’s modulus, compared to the original architecture. This work establishes a link between the physical properties at micro-nanoscale and macroscale structures, which provides inspirations for high load-bearing auxetic metamaterials.

A multiscale nonlocal continuum model is proposed to describe the superelastic deformation of gradient nano-grained NiTi shape memory alloys (SMAs). At the mesoscopic scale, the polycrystalline aggregate is regarded as a composite, i.e., the grain-interior (GI) phase is assumed to be a cuboidal inclusion embedded in a matrix of grain-boundary (GB) phase. An intrinsic energetic length and a gradient energy are introduced into the Helmholtz free energy of the GI phase. The criterion of martensite transformation (MT) is derived based on the principle of virtual power and second law of thermodynamics. The hindering effect of GB on MT in GI phase is addressed. By deriving the analytical solution of the proposed model and introducing a scale transition rule, the overall and local stress-strain responses of the specimen at the macroscopic scale are obtained. The prediction capability of the proposed model is verified by comparing the analytical solution with the experiment. The influences of the distribution form for the grain size (GS) on the superelastic deformation of gradient nano-grained NiTi SMAs are further predicted and discussed. The analytical form and low computational cost of the proposed model make it an appropriate theoretical tool to design the gradient nano-grained SMAs with desired mechanical property.

In order to solve the vertical vibration negative effect problem caused by the increase of the unsprung mass in the hub motor driven vehicle (HMDV), a novel mechatronic suspension using the bridge electrical network is proposed. Firstly, the bridge electrical networks composed of two capacitors, two inductors, and one resistor are summarized and their impedance functions are analyzed forward through the structural method. Then a quarter HMDV model is constructed, and the optimal element parameters in the electrical networks are selected through the Pattern Search algorithm. The influence of element parameters perturbation of the optimal structure on the output response of HMDV suspension is further analyzed. Results show that the proposed bridge electrical network can be realized as a biquartic impedance. It can be equivalent to a mechanical impedance of the suspension through a linear motor. Compared with the conventional suspension, the root-mean-square values of the dynamic tire load and the suspension working space are reduced by 10.76% and 18.10%, respectively. The vibration at low and high frequencies of the unsprung mass is suppressed, effectively improving the grounding and handling stability of the vehicle.