Biomimetics最新文献

The loofah sponge has a complex, three-dimensional, porous mesh fiber structure characterized by markedly low density and excellent vibration isolation properties. In this study, loofah sponges made from dried Luffa cylindrica were divided into two components: the core unit and the shell unit, which were further subdivided into five regions. Static compression performance tests and vibration isolation analysis were conducted on the loofah sponge and its individual parts. Scanning models of the loofah sponge were generated using the RX Solutions nano-CT system in France, and finite element analysis was performed using the ANSYS Workbench. This study focused on the vibration isolation performance of the loofah sponge, examining energy absorption and isolation, as well as the vibrational strength of its isolation performance. The goal was to explore the functions and vibration isolation mechanisms of its different components. The results demonstrated that the loofah sponge structure exhibits rigid-flexible coupling, with the coordinated action of multiple parts producing highly effective energy absorption and isolation of the vibration intensity effect. Specifically, the core unit of the loofah sponge provides the best isolation effect of axial vibration intensity, with an acceleration vibration transfer of -60 dB at 300 Hz. Furthermore, both the core and shell unit structures combine to provide multidirectional low-frequency vibration isolation. This study of the loofah sponge's vibration isolation mechanism provides a theoretical foundation and new insights for the design of bionic low-frequency vibration isolation devices.

Slender tubes are in high demand owing to their lightweight and outstanding energy absorption. However, conventional slender tubes are prone to catastrophic failures such as Euler's buckling under axial load. Interestingly, growing bamboos overcome this similar dilemma via a unique tapered intine in the internodes, which endows them with excellent energy absorption. Inspired by this finding, a bionic inner-tapered tube (BITT) was designed to enhance the energy absorption of slender tubes under axial load. The special energy absorption (SEA) was evaluated via a quasi-static axial compression test. Then, theoretical calculation and finite element analysis were carried out to analyze the energy absorption mechanisms. The results reveal that the tapered inner wall induces a progressively enhanced fold deformation mode for BITT, which not only prevents buckling failure and decreases initial peak crushing load but also improves the energy absorption efficiency by increasing plastic deformation. The influences of taper and length-diameter ratio on the axial energy absorption of BITT are explored. Finally, the bionic square array (BSA) and bionic hexagon array (BHA) are fabricated by taking BITT as the basic structural unit, which significantly improves the main energy absorption performance indicators under axial load.

Numerous studies have investigated the surface treatment of implants using various types of plasma, including atmospheric pressure plasma and vacuum plasma, to remove impurities and increase surface energy, thereby enhancing osseointegration. Most previous studies have focused on generating plasma directly on the implant surface by using the implant as an electrode for plasma discharge. However, plasmas generated under atmospheric and moderate vacuum conditions often have a limited plasma volume, meaning the shape of the electrodes significantly influences the local electric field characteristics, which in turn affects plasma behavior. Consequently, to ensure consistent performance across implants of different sizes and shapes, it is essential to develop a plasma source with discharge characteristics that are unaffected by the treatment target, ensuring uniform exposure. To address this challenge, we developed a novel plasma source, termed "vortex plasma", which generates uniform plasma using a magnetic field within a controlled space. We then compared the surface treatment efficiency of the vortex plasma to that of conventional direct plasma discharge by evaluating hydrophilicity, surface chemistry, and surface morphology. In addition, to assess the biological outcomes, we examined osteoblast cell activity on both the vortex and direct plasma-treated surfaces. Our results demonstrate that vortex plasma improved hydrophilicity, reduced carbon content, and enhanced osteoblast adhesion and activity to a level comparable to direct plasma, all while maintaining the physical surface structure and morphology.

This study presents a novel rod-cable hybrid planar cable-driven parallel robot inspired by the biological synergy of bones and muscles. The design integrates rigid rods and flexible cables to enhance structural stability and precision in motion control. The rods emulate bones, providing foundational support, while the cables mimic muscles, driving motion through coordinated tension. This design enables planar motions with three degrees of freedom, and a structural configuration that mitigates sagging and vibration for improved stability and accuracy by introducing rigid structure. The study develops detailed kinematic models, including Jacobian analysis for motion control, and evaluates the workspace using geometric and Monte Carlo methods.

The shape design and optimization of complex disk curves is a crucial and intractable technique in computer-aided design and manufacturing (CAD/CAM). Based on disk Wang-Ball (DWB) curves, this paper defines a novel combined disk Wang-Ball (CDWB) curve with constrained parameters and investigates the shape optimization of CDWB curves by using the multi-strategy ameliorated chameleon swarm algorithm (MCSA). Firstly, in order to meet the various shape design requirements, the CDWB curves consisting of n DWB curves are defined, and the G¹ and G² geometric continuity conditions for the curves are derived. Secondly, the shape optimization of CDWB curves is considered as a minimization problem with curve energy as the objective, and an optimization model is developed under the constraints of the splicing conditions. Finally, the meta-heuristic algorithm MCSA is introduced to solve the established optimization model to obtain the minimum energy value, and its performance is verified by comparison with other algorithms. The results of representative numerical examples confirm the effectiveness and competitiveness of the MCSA for the CDWB curve shape optimization problems.

The current research aims to analyze the shape and structural features of the eggs of the lepidoptera species Melitaea sp. (Lepidoptera, Nympalidae) and develop design solutions through the implementation of a novel strategy of biomimetic design. Scanning electron microscopy (SEM) analysis of the chorion reveals a medial zone that forms an arachnoid grid resembling a ribbed dome with convex longitudinal ribs and concave transverse ring members. A parametric design algorithm was created with the aid of computer-aided design (CAD) software Rhinoceros 3D and Grasshopper3D in order to abstract and emulate the biological model. A series of physical models were manufactured with variations in geometric parameters like the number of ribs and rings, their thickness, and curvature. Selective laser sintering (SLS) technology and Polyamide12 (nylon) material were utilized for the prototyping process. Quasi-static compression testing was carried out in conjunction with finite element analysis (FEA) to investigate the deformation patterns and stress dispersion of the models. The biomimetic ribbed dome appears to significantly dampen the snap-through behavior that is observed in typical solid and lattice domes, decreasing dynamic stresses developed during the response and preventing catastrophic failure of the structure. Increasing the curvature of the ring segments further reduces the snap-through phenomenon and improves the overall strength. However, excessive curvature has a negative effect on the maximum sustained load. Increasing the number and thickness of the transverse rings and the number of the longitudinal ribs also increases the strength of the dome. However, excessive increase in the rib radius leads to more acute snap-through behavior and an earlier failure. The above results were validated using respective finite element analyses.

Despite technological advancements in various industries, the equine sector still relies on old methods like horseshoeing. Although traditional, the industry is dynamic and well-funded. Therefore, there is a need to modernize these methods with more reliable and less invasive solutions for attaching horseshoes to horse hooves. There are currently several commercial adhesive solutions in the market specifically tailored to this application. In this work, the mechanical properties of two acrylic adhesives were characterized under quasi-static conditions. In the characterization process, tensile, shear, and fracture properties were determined. Subsequently, in-joint behavior was assessed using single-lap joints (SLJ) for both similar and dissimilar adherends. The adherends' materials included steel (St), aluminum (Al), and horse hoof wall (HW), and the following adherend combinations were tested: St-St, Al-Al, and St-HW. A numerical model of similar joints was developed and validated based on experimental results. After its validation, the next steps are the modelling of the real joint and its simulation by considering realistic loading conditions in order to determine the weakest points of the joint. This exploratory study seeks to establish a foundation for investigating alternative adhesive solutions that could address the limitations identified in the solutions studied in this paper.

Surface electromyography (sEMG) signals reflect the local electrical activity of muscle fibers and the synergistic action of the overall muscle group, making them useful for gesture control of myoelectric manipulators. In recent years, deep learning methods have increasingly been applied to sEMG gesture recognition due to their powerful automatic feature extraction capabilities. sEMG signals contain rich local details and global patterns, but single-scale convolutional networks are limited in their ability to capture both comprehensively, which restricts model performance. This paper proposes a deep learning model based on multi-scale feature fusion-MS-CLSTM (MS Block-ResCBAM-Bi-LSTM). The MS Block extracts local details, global patterns, and inter-channel correlations in sEMG signals using convolutional kernels of different scales. The ResCBAM, which integrates CBAM and Simple-ResNet, enhances attention to key gesture information while alleviating overfitting issues common in small-sample datasets. Experimental results demonstrate that the MS-CLSTM model achieves recognition accuracies of 86.66% and 83.27% on the Ninapro DB2 and DB4 datasets, respectively, and the accuracy can reach 89% in real-time myoelectric manipulator gesture prediction experiments. The proposed model exhibits superior performance in sEMG gesture recognition tasks, offering an effective solution for applications in prosthetic hand control, robotic control, and other human-computer interaction fields.

Objective: We previously developed artificial intelligence (AI) diagnosis algorithms for predicting the six classes of stomach lesions. However, this required significant computational resources. The incorporation of AI into medical devices has evolved from centralized models to decentralized edge computing devices. In this study, a deep learning endoscopic image classification model was created to automatically categorize all phases of gastric carcinogenesis using an edge computing device.

Design: A total of 15,910 endoscopic images were collected retrospectively and randomly assigned to train, validation, and internal-test datasets in an 8:1:1 ratio. The major outcomes were as follows: 1. lesion classification accuracy in six categories: normal/atrophy/intestinal metaplasia/dysplasia/early/advanced gastric cancer; and 2. the prospective evaluation of classification accuracy in real-world procedures.

Results: The internal-test lesion-classification accuracy was 93.8% (95% confidence interval: 93.4-94.2%); precision was 88.6%, recall was 88.3%, and F1 score was 88.4%. For the prospective performance test, the established model attained an accuracy of 93.3% (91.5-95.1%). The established model's lesion classification inference speed was 2-3 ms on GPU and 5-6 ms on CPU. The expert endoscopists reported no delays in lesion classification or any interference from the deep learning model throughout their exams.

Conclusions: We established a deep learning endoscopic image classification model to automatically classify all stages of gastric carcinogenesis using an edge computing device.

Direct-drive servo systems are extensively applied in biomimetic robotics and other bionic applications, but their performance is susceptible to uncertainties and disturbances. This paper proposes an adaptive disturbance rejection Zeta-backstepping control scheme with adjustable damping ratios to enhance system robustness and precision. An iron-core permanent magnet linear synchronous motor (PMLSM) was employed as the experimental platform for the development of a dynamic model that incorporates compensation for friction and cogging forces. To address model parameter uncertainties, an indirect parameter adaptation strategy based on a recursive least squares algorithm was introduced. It updates parameters based on the system state instead of output error, ensuring robust parameter convergence. An integral sliding mode observer (ISMO) was constructed to estimate and compensate for residual uncertainties, achieving finite-time state estimation. The proposed Zeta-backstepping controller enables adjustable damping ratios through parameterized control laws, offering flexibility in achieving desired dynamic performance. System stability and bounded tracking performance were validated via a second-order Lyapunov function analysis. Experimental results on a real PMLSM platform demonstrated that, while achieving adjustable damping ratio dynamic characteristics, there is a significant improvement in tracking accuracy and disturbance suppression. This underscores the scheme's potential for advancing precision control in biomimetic robotics and other direct-drive system applications.