Membrane-type acoustic metamaterials (MAMs) have recently received widespread attention due to their good low-frequency sound-transmission-loss (STL) performance. A fast prediction method for the STL of rectangular membranes loaded with masses of arbitrary shapes and surface density values is proposed as a semi-analytical model for calculating the STL of membrane-type acoustic metamaterials. Through conformal mapping theory, the mass blocks of arbitrary shapes were transformed into regular shapes, which simplified the calculation model of acoustic propagation loss prediction, and the prediction results were verified by finite element simulations. The results show that the change in mass surface density was closely related to the size and frequency distribution of STL. The influence of the mass center on the STL and characteristic frequency of the film metamaterial is discussed.

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.

Traditional sound-absorbing materials, which are intended to address the issue of low-frequency noise control in automobile air-conditioning duct mufflers, have limited noise reduction effects in small spaces. Because of their straightforward structure and excellent controllability, acoustic metamaterials—particularly Helmholtz resonators—have emerged as a research hotspot in low-frequency noise reduction. However, existing technologies have issues such as restricted structural scale, narrow absorption frequency bands, and conflicts with ventilation requirements. To address these, this paper proposes a new type of Helmholtz perforated and tortuous-characteristic duct muffler for the unit cell of acoustic metamaterials. Through the innovative structural design combining a perforated panel with a multi-channel tortuous cavity, the length of the channel is changed in a limited space, thereby extending the sound wave propagation path and enhancing the dissipation of sound wave energy. Meanwhile, for the muffler, acoustic theoretical modeling, finite element simulation, and parametric optimization methods are adopted to systematically analyze the influence of its key structural parameters on the sound transmission loss (STL) of the muffler. Compared with the traditional folded-channel metamaterial, the two differ in resonance frequency by 38 Hz, in transmission loss by 1.157 dB, and in effective bandwidth by 1 Hz. This research provides theoretical support and design basis for solving the problem of low-frequency noise control in ventilation ducts, improves low-frequency broadband sound absorption performance, and promotes the engineering application of high-efficiency noise reduction devices.

Fillet welds of highly reflective materials are common in industrial production. It is a great challenge to accurately locate the fillet welds of highly reflective materials. Therefore, this paper proposes a fillet weld identification and location method that can overcome the negative effects of high reflectivity. The proposed method is based on improving the semantic segmentation performance of the DeeplabV3+ network for structural light and reflective noise, and, with MobilnetV2, replaces the main trunk network to improve the detection efficiency of the model. To solve the problem of the irregular and discontinuous shapes of the structural light skeleton extracted by traditional methods, an improved closing operation using dilation in a combined Zhang-suen algorithm was proposed for structural light skeleton extraction. Then, a three-dimensional reconstruction as a mathematical model of the system was established to obtain the coordinates of the weld feature points and the welding-torch angle. Finally, many experiments on highly reflective stainless steel fillet welds were carried out. The experimental results show that the average detection errors of the system in the Y-axis and Z-axis are 0.3347 mm and 0.3135 mm, respectively, and the average detection error of the welding torch angle is 0.1836° in the test of a stainless steel irregular fillet weld. The method is robust, universal, and accurate for highly reflective irregular fillet welds.

Aiming to achieve resonance suppression of a controllable mechanism welding robot end using dynamic modeling and experimental verification, this paper applies a central composite design methodology to optimize suppression resonance using three variables: excitation source, rod stiffness and damping. Considering the coupling effects of the excitation source, member stiffness and damping, a combination optimization is carried out, and the optimal control result of the end is obtained, for which the optimized peak amplitudes are significantly reduced. The research provides an important technical basis for the dynamic design and resonance suppression control of controllable welding robot mechanisms.