Rolling bearings are an important part of rotating machinery, and are of great significance for fault diagnosis and life monitoring of rolling bearings. Analyzing fault signals, extracting effective degradation information and establishing corresponding models are the premise of residual life prediction of rolling bearings. In this paper, first, the time-domain features were extracted to form the eigenvector of the vibration signal, and then the index representing the bearing degradation was found. It was found that the time-domain index could effectively describe the degradation information of the bearing, and the multi-dimensional time-domain characteristic information could effectively describe the attenuation trend of the vibration signal of the rolling bearing. On this basis, appropriate feature vectors were selected to describe the degradation characteristics of bearings. Aiming at the problems of large amounts of data, large amounts of information redundancy and unclear performance index of multi-dimensional feature vectors, the dimensionality of multi-dimensional feature vectors was reduced with principal component analysis, thus, simplifying the multi-dimensional feature vectors and reducing the information redundancy. Finally, in view of the support vector machine (SVM)’s needs to determine kernel function parameters and penalty factors, the squirrel optimization algorithm (SOA) was used to adaptively select parameters and establish the state-life evaluation model of rolling bearings. In addition, mean absolute error (MAE) and root mean squared error (RMSE) were used to comprehensively evaluate SOA. The results showed that the SOA reduced the errors by 5.1% and 13.6%, respectively, compared with a genetic algorithm (GA). Compared with particle swarm optimization (PSO), the error of SOA was reduced by 7.6% and 15.9%, respectively. It showed that SOA-SVM effectively improved the adaptability and regression performance of SVM, thus, significantly improving the prediction accuracy.

Three-dimensional printing is a transformative technology in the manufacturing industry which provides customization and cost-effectiveness for all walks of life due to its fast molding speed, high material utilization, and direct molding of arbitrary complex structural parts. This study aims to improve the molding accuracy of 3D printed polyether ether ketone (PEEK) samples by systematically studying key process parameters, including printing speed, layer thickness, nozzle temperature, and filling rate. The 3D printing nozzle has an important impact on the extrusion rate of the melt, and the fluid simulation of the nozzle was carried out to explore the variation characteristics of the melt flow rate in the nozzle and optimize the nozzle structure parameters. In order to effectively optimize the process, considering its inherent efficiency, robustness, and cost-effectiveness, the L9 orthogonal array experimental design scheme was used to analyze the effects of printing speed, layer thickness, nozzle temperature, and filling rate on the molding accuracy of the test sample, and the optimal combination of process parameters was optimized through the comprehensive weighted scoring method so as to improve the molding accuracy of the 3D printed PEEK sample; finally, the molding accuracy of the components printed using the Sermoon-M1 3D printer with the optimized nozzle structure was printed. The results show that the nozzle structure is optimal when the convergence angle is 120° and the aspect ratio is 2, and the outlet cross-section velocity is increased by 2.5% and 2.7%, respectively. The order of influence strength on the dimensional accuracy of the test sample is layer thickness > filling rate > nozzle temperature > printing speed. The optimal combination of parameters is: a printing speed of 15 mm/s, a layer thickness of 0.1 mm, a nozzle temperature of 420 °C, and a filling rate of 50%. The insights derived from this study pave the way for predicting and implementing the selection of optimal process parameters in the production of 3D printed products, with important implications for the optimal molding accuracy of printed components.

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.

Honeycomb core sandwich plates are widely used as a lightweight, high-strength sound insulation material. However, they do not perform well in specific frequency bands. Acoustic metamaterials can break the law of mass in specific frequency bands and have high sound transmission loss (STL); however, the resonance frequency is difficult to regulate. To solve this problem, this paper first proposes an infinitely large metamaterial honeycomb core sandwich plate, which can generate newly tuned piezoelectric resonance frequencies, and we study its STL. The structure has piezoelectric patches arranged in sub-wavelength arrays with inductance shunting circuits that are elastically connected to both sides of the honeycomb core sandwich plate. The effective dynamic mass density and effective dynamic bending stiffness of the metamaterial plates were obtained using the effective medium (EM) method. A theoretical model for the numerical calculation of oblique STL and diffuse-field STL was established by the structural bending wave method. The finite element simulation method was used to verify that the metamaterial plates can generate three peaks at 1147 Hz, 1481 Hz and 1849 Hz in oblique or diffuse-field STL curves, which reached 57 dB, 86 dB and 63 dB, respectively, and are significantly better than the plate rigidly connected with piezoelectric sheets and the bare plate with the same mass. In order to better understand the characteristics of STL, the explicit functions of the resonance frequencies were derived. Key influencing factors were analyzed, and the regulation law of new piezoelectric resonance frequencies was clarified.

A gradient composite laminate that was composed of two-phase fibers, a shape memory alloy (SMA), and graphite was prepared to investigate modal performance and improve vibration behavior. The stress-strain relation of the single-layer composite plates was derived from Kirchhoff thin plate theory and the material constitutive of the SMA. A gradient distribution model and the eigenvalue equations of gradient composite laminates were developed. The influence of the fiber component content gradient distribution, pre-strain, the two-phase fiber volume fraction, and geometric parameters on the modal performance was analyzed. This study provides a method to avoid the structural resonance of composite laminates that are embedded with an SMA through the gradient distribution of two-phase fiber content that leads to the interaction of the material properties.

Two algorithms that are distinct from the closed algorithm are proposed to create the inverse kinematics model of the UR10 robot: the Sequential Quadratic Programming (SQP) algorithm and the Back Propagation-Sequential Quadratic Programming (BP-SQP) algorithm. The SQP algorithm is an iterative algorithm in which the fundamental tenet is that the joint’s total rotation radian should be at a minimum when the industrial robot reaches the target attitude. With this tenet, the SQP algorithm establishes the inverse kinematics model of the robot. Since the SQP algorithm is overly reliant on the initial values, deviations occur easily and the solution speed, and the accuracy of the algorithm is undermined. To assuage this disadvantage of the SQP algorithm, a BP-SQP algorithm incorporating a neural network is introduced to optimize the initial values. The results show that the SQP algorithm is an iterative algorithm that relies excessively on the initial values and has a narrow range of applications. The BP-SQP algorithm eliminates the limitations of the SQP algorithm, and the time complexity of the BP-SQP algorithm is greatly reduced. Subsequently, the effectiveness of the SQP algorithm and the BP-SQP algorithm is verified. The results show that the SQP and BP-SQP algorithms can significantly reduce the operation time compared with the closed algorithm, and the BP-SQP algorithm is faster but requires a certain number of samples as a prerequisite.