Product performance, function, cost, and the level of module generalization are all significantly influenced by product modular design, but different goods require different division indicators and techniques. The purpose of this study is to provide a set of appropriate modular division techniques for FDM 3D printers. This research offers an ecologically friendly module division index and uses module clustering as the module division principle in accordance with the current industrial development trend and the fundamental requirements of FDM 3D printer consumers in the current market. The K-means algorithm is used to use the Jaccard similarity coefficient as the metric of similarity of the DSM clustering process to realize the module division of the FDM 3D printer after studying the function–behavior–structure mapping model of the 3D printer. Additionally, the elbow method–cluster error variance and average contour coefficient evaluation systems were built, respectively, in order to verify the viability of the FDM 3D printer module division method and obtain the best module division results. By analyzing these two systems, it was discovered that when the FDM 3D printer was divided into three modules, the in-cluster error variance diagram obviously had an inflection point, and the average profile coefficient and other modular approaches that need to be adjusted to their respective goods can use this division method as a theoretical foundation and point of reference.

For conducting scientific research at depths in the ocean, deep-sea probes are essential pieces of equipment. The cylindrical shell is the most sensible and rational packaging structure for these detectors. New technical challenges for enhancing the pressure resistance and lightweight design of the pressure-resistant cylindrical shell arise from the need to ensure that the detector packaging structure can withstand the immense water pressure at tens of thousands of meters in the underwater environment, while simultaneously reducing the detector packaging structure’s self-weight. This article examines the detection system’s deep-sea pressure-resistant cylindrical shell. To address the issue of the pressure-resistant shell’s insufficient ability to counteract the overall instability caused by the inability to form unstable half-waves in the radial direction when the ring rib pressure-resistant shell experiences it, a design method for the ribs inside the unequal-stiffness pressure-resistant cylindrical shell is suggested. The shell’s instability pressure increases by 9.65 MPa following the stiffness adjustment. Simultaneously, in order to attain even more lightweight optimization, the optimal inner rib section was obtained by applying the orthogonal topology optimization method, which also reduced the weight by 106.8 g and effectively improved the compression stability of the high-pressure cylindrical shell structure. Based on this, key optimization variables were found by performing sensitivity analysis on the cylindrical shell structure’s parameters. Then, with lightweighting as the primary objective, the high-pressure-resistant cylindrical shell’s optimal structural parameters were found using a multi-objective optimization process using the second-generation fast non-dominated genetic algorithm (NSGA-2). This resulted in a weight reduction of 1.2492 kg, or 17.26% of the original pressure-resistant shell. This has led to the development of a lightweight, highly pressure-resistant method for packaging marine exploration equipment structures.

Acoustic metamaterials are artificially engineered materials composed of subwavelength structural units, whose effective acoustic properties are primarily determined by structural design rather than intrinsic material composition. By introducing local resonances, these materials can exhibit unconventional acoustic behavior, enabling enhanced sound insulation beyond the limitations of conventional structures. In this study, a thin plate (thin sheet) refers to a structural element whose thickness is much smaller than its in-plane dimensions and can be accurately described using classical thin-plate vibration theory. When resonant mass blocks are attached to a thin plate, a thin-plate acoustic metamaterial is formed through the coupling between plate bending vibrations and local resonances. Thin-plate acoustic metamaterials exhibit excellent sound insulation performance in the low- and mid-frequency ranges. Multilayer configurations and the combination with porous materials can effectively broaden the insulation bandwidth and improve overall performance. However, the large number of structural parameters in multilayer composite thin-plate acoustic metamaterials significantly increases design complexity, making conventional trial-and-error approaches inefficient. To address this challenge, a neural-network-based inverse design framework is proposed for multilayer composite thin-plate acoustic metamaterials. An analytical model of thin-plate metamaterials with multiple attached cylindrical masses is established using the point matching and modal superposition methods and validated by finite element simulations. A multilayer composite unit cell is then constructed, and a dataset of 30,000 samples is generated through numerical simulations. Based on this dataset, a forward prediction network achieves a test error of 1.06%, while the inverse design network converges to an error of 2.27%. The inverse-designed structure is finally validated through impedance tube experiments. The objective of this study is to establish a systematic theoretical and neural-network-assisted inverse design framework for multilayer thin-plate acoustic metamaterials. The main novelties include the development of an accurate analytical model for thin-plate metamaterials with multiple attached masses, the construction of a large-scale simulation dataset, and the proposal of a neural-network-assisted inverse design strategy to address non-uniqueness in inverse design. The proposed approach provides an efficient and practical solution for low-frequency sound insulation design.

The cutterhead torque of a full-face tunnel boring machine (TBM) is a pivotal parameter that characterises the rock-machine interaction. Its dynamic prediction is of considerable significance to achieve intelligent regulation of the boring parameters and enhance the construction efficiency and safety. In order to achieve high-precision time series prediction of cutterhead torque under complex geological conditions, this study proposes an intelligent prediction method (VBGAP) that integrates signal decomposition mechanism and physical constraints. At the data preprocessing level, a multi-step data cleaning process is designed. This process comprises the following steps: the processing of invalid values, the detection of outliers, and normalisation. The non-smooth torque time-series signal is decomposed by variational mode decomposition (VMD) into narrow-band sub-signals that serve as a data-driven, frequency-specific input for subsequent modelling, and a hybrid deep learning model based on Bi-GRU and self-attention mechanism is built for each sub-signal. Finally, the prediction results of each component are linearly superimposed to achieve signal reconstruction. Concurrently, a novel modal energy conservation loss function is proposed, with the objective of effectively constraining the information entropy decay in the decomposition-reconstruction process. The validity of the proposed method is supported by empirical evidence from a real tunnel project dataset in Northeast China, which demonstrates an average accuracy of over 90% in a multi-step prediction task with a time step of 30 s. This suggests that the proposed method exhibits superior adaptability and prediction accuracy in comparison to existing mainstream deep learning models. The findings of the research provide novel concepts and methodologies for the intelligent regulation of TBM boring parameters.

At present, although there are many SCARA manipulator solutions with vertical lifting functionality, they generally suffer from high maintenance costs and complex structures. Moreover, systematic performance evaluations based on international standards are lacking, leading to unclear critical performance boundaries such as accuracy and payload in practical applications. To address these issues, this paper designs and manufactures a low-cost SCARA manipulator for educational and research demonstrations as well as light-duty electronic parts assembly scenarios. A “leadscrew + stepper motor” scheme is adopted for vertical lifting, and an Arduino Mega 2560 development board serves as the core controller, significantly reducing system cost. A three-dimensional model is established using SolidWorks 2022, and kinematic simulations are carried out with MATLAB 2024a to preliminarily verify the feasibility of the mechanism. Subsequently, a physical prototype is built and experimental tests are conducted in accordance with the ISO 9283 standard. The experimental results show that the repeatability of the manipulator is controlled within the range of 0.05–0.3 mm, the path deviation caused by vibration lies between −0.52 mm and 0.3 mm, and the maximum payload capacity is 3.91 N. These experimental data can serve as a benchmark for the design and performance comparison of similar low-cost manipulators.