In the present work, an explicit finite element (FE) model was developed for predicting cutting forces and chip morphologies of polymers from the true stress–strain curve. A dual fracture process was used to simulate the cutting chip formation, incorporating both the shear damage failure criterion and the yield failure criterion, and considering the strain rate effect based on the Johnson–Cook formulation. The frictional behaviour between the cutting tool and specimen was defined by Coulomb’s law. Further, the estimated cutting forces and chip thicknesses at different nominal cutting depths were utilized to determine the fracture toughness of the polymer, using an existing mechanics method. It was found that the fracture toughness, cutting forces, and chip morphologies predicted by the FE model were consistent with the experimental results, which proved that the present FE model could effectively reflect the cutting process. In addition, a parametrical analysis was performed to investigate the effects of cutting depth, rake angle, and friction coefficient on the cutting force and chip formation, which found that, among these parameters, the friction coefficient had the greatest effect on cutting force.

A bio-inspired vehicle with banded fin fluctuation as the propulsion mode is the research topic. However, this propulsion mode suffers from low efficiency and requires the urgent resolution of other issues. In this paper, the kinematic model of the banded fin surface and the numerical calculation model for its hydrodynamic performance are established based on the long dorsal fin propelled by MPF (Media and/or Paired Fin propulsion) mode. Through numerical simulation, the hydrodynamic performance of the banded fin under typical working conditions is explored and its propulsion mechanism is analyzed. By using a method of controlling variables, such as wave number, swing angle, and frequency, where only one independent variable is changed at a time while the others remain constant, the impact on thrust coefficient function and the obtained periodic variation laws governing hydrodynamic performance are studied. Oscillatory thrust is generated by the fin’s motion, where it first captures water through a ‘scoop’ motion and then expels it via a diagonal ‘push’ motion, producing thrust. Due to limitations in fin length and varying oscillation shapes, the effective water-pushing stroke differs, leading to variations in work and creating periodic oscillatory forces. When the variable is the oscillation frequency, the propulsion efficiency of the oscillating fins remains nearly constant when the oscillation frequency is less than or equal to 1 Hz. However, when the oscillation frequency exceeds 1 Hz, the propulsion efficiency decreases as the oscillation frequency increases, and the rate of decrease gradually slows down. The effect of leading-edge suction on hydrodynamic performance was studied by varying the oscillating fin’s angle of attack. The results showed that, compared to the unchamfered configuration, the forward chamfer better utilizes vortex energy, reducing input power and significantly improving propulsion efficiency. Guided by both numerical simulations and experimental results, we design and manufacture a prototype of an underwater banded fin bio-inspired propeller that encompasses shape modeling, mechanical structure design, and control mechanism design. We conduct real water tests to verify feasibility and reliability in terms of forward movement, backward movement, and turning ability, among others. Furthermore, we analyze how varying angle of attack or optimizing front/rear edge shapes can effectively enhance hydrodynamic performance.

The self-excited oscillation of an electro-hydraulic servo valve is a crucial factor that affects the operational stability of both the servo valve and the entire servo system. As the intermediary for electromagnetic force, hydraulic force, and feedback force, the dynamic characteristics of the armature assembly play a pivotal role in the emergence of self-excited oscillations. In this article, a mathematical model of the armature assembly is developed using the finite element method (FEM), which establishes a connection between the input signal and the dynamic characteristics of the armature assembly. The structure of the parameter matrix and the recursive relationship of discrete time steps are derived, while the discrete method of the feedback rod and the discrete time format of the FEM model are also analyzed to enhance the prediction accuracy of the model. To validate the efficacy of the FEM model, a numerical simulation model, a classical mathematical model, and an experimental platform are constructed to simulate or measure the dynamic characteristics of the armature assembly. Through the comparison of the dynamic characteristics obtained by the abovementioned methods, it is confirmed that the FEM model effectively predicts both the static and dynamic characteristics of the armature assembly. To suppress the vibration magnitude within the natural frequency range of the armature assembly, the external load that can effectively mitigate the resonance amplitude is calculated based on the FEM model, and the aim of ensuring the stable functionality of the armature assembly throughout its operational range is achieved.

Automatic classification of fish is essential for developing marine ecology, behavior analysis, aquaculture management, and health monitoring. However, the existing underwater categorization detection technology is far from keeping up with the escalating demands. In this study, Deepwater-SE based on the Squeeze-and-Excitation mechanism (SE) was proposed to classify fish with an unrestricted and real natural environment. To improve the efficiency of the network, the Depthwise Separable Convolution and the SE-ResNet were added, and at the same time, the detailed analysis module and the feature extraction module were designed before the backbone network specifically. A total of 27,370 photos of Fish4Knowledge(F4K) datasets were used for training and testing. The experimental results demonstrated that the proposed model had the highest mean accuracy of 99.58% among nine comparison models, including LDA+SVM, Raw-Pixel SVM, Raw-Pixel Softmax, Raw-Pixel Nearest Neighbour, Deep fish-Softmax-Aug, VLFeat Dense-SIFT, Alex-FT-Soft, Deep Fish-SVM, Deep-CNN. In addition, the proposed method also led to better results by providing excellent rates of Precision, Recall, and F1, respectively. All the results indicated that Deepwater-SE could categorize fish properly and effectively while showing great robustness and accuracy in large datasets.

Underwater image enhancement (UIE) is crucial for underwater perception tasks but is challenged by complex physical degradations, including backscatter, wavelength-dependent attenuation, scattering, turbidity, and color cast. These factors severely reduce visibility and color fidelity in underwater scenes. This article presents a UIE method called UIE-DDPM, which is based on the conditional denoising diffusion probabilistic model and underwater physical model. UIE-DDPM innovatively enhances underwater images by integrating the Jaffe-McGlamery underwater physical model with the diffusion process and employing optical compensation as a conditional controller to regulate each iteration step with greater specificity. The UIE-DDPM consists of the variational autoencoder–optical compensation prediction network (VCP) and the underwater image semantic enhancement network (UISE). The VCP establishes the analytical relationship between optical compensation levels, performs Gaussian sampling and parameter renormalization, and estimates the distribution of optical compensation losses in the latent space. The UISE effectively enhances the essential semantics of degraded images by utilizing reference color-enhanced images as auxiliary information, thereby improving the understanding of details, contours, and contrasts. Meanwhile, the UIE-DDPM outperforms other baseline methods and generative adversarial network models in UIE, establishing a new state-of-the-art benchmark. In real-world application scenarios, the maximum underwater image quality metric increased by 1.336, and the maximum underwater color image quality evaluation increased by 0.158, demonstrating the effectiveness of the proposed method in enhancing underwater image quality under turbid conditions.