A multi-objective optimal model of a K-H-V cycloid pin gear planetary reducer is presented in this article. The optimal model is established by taking the objective functions of the reducer volume, the force of the turning arm bearing, and the maximum bending stress of the pin. The optimization aims to decrease these objectives and obtains a set of Pareto optimal solutions. In order to improve the spread of the Pareto front, the density estimation metric (crowding distance) of non-dominated sorting genetic algorithm II is replaced by the k nearest neighbor distance. Then, the improved algorithm is used to solve this optimal model. The results indicate that the modified algorithm can obtain the better Pareto optimal solutions than the solution by the routine design.
The air bearing motorized spindle (ABMS) is the key component of the ultra-precision machine tool, which plays an important role in the ultra-precision machining process and directly influences machining accuracy. The influence of unbalanced magnetic force (UMF) on the nonlinear dynamic behavior of the ABMS is not understood clearly. To reveal the potential influence of the UMF, a mathematical model of the ABMS considering multiphysics fields is established. The variation trend of the UMF is simulated, and the nonlinear dynamic behavior of the ABMS is analyzed which emphasizes on the stability of the rotating shaft. It is shown that the UMF varies linearly at large rotor eccentricity which meets well with previous research, but it is noteworthy the UMF varies nearly to a quadratic function at small rotor eccentricity. The result of rotor dynamics shows that the UMF can change the converge position of the rotor center and the converge speed. Moreover, when at certain rotor mass and external load, the UMF can enlarge the stability boundary of the rotor. This research provides an example of analyzing the nonlinear dynamic behavior of the ABMS considering multiphysics fields which may help to the further investigation.
Microwave induced plasma torches find wide applications in material and chemical analysis. Investigation of a coaxial electrode microwave induced plasma (CE-MIP) torch is conducted in this study, making it available for glass surface modification and polishing. A dedicated nozzle is designed to inject secondary gases into the main plasma jet. This study details the adaptation of a characterisation process for CE-MIP technology. Microwave spectrum analysis is used to create a polar plot of the microwave energy being emitted from the coaxial electrode, where the microwave energy couples with the gas to generate the plasma jet. Optical emission spectroscopy analysis is also employed to create spatial maps of the photonic intensity distribution within the plasma jet when different additional gases are injected into it. The CE-MIP torch is experimentally tested for surface energy modification on glass where it creates a super-hydrophilic surface.
The aerostatic spindle in the ultra-precision machine tool shows the complex multi-field coupling dynamics behavior under working condition. The numerical investigation helps to better understand the dynamic characteristics of the aerostatic spindle and improve its structure and performance with low cost. A multi-field coupling 5-DOF dynamics model for the aerostatic spindle is proposed in this paper, which considers the interaction between the air film, spindle shaft and the motor. The restoring force method is employed to deal with the times varying air film force, the transient Reynolds equation of the aerostatic journal bearing and the aerostatic thrust bearing is solved using ADI method and Thomas method. The transient air film pressure of aerostatic bearings is obtained which clearly presents the influence induced by the tilt motion of the spindle shaft. The motion trajectory of the spindle shaft is obtained which shows different stability of the shaft under different external forces. The dynamics model shows good performance on simulating the multi-field coupling behavior of the aerostatic spindle under external force. which is quite meaningful and useful for the further research on the dynamic characteristics of the aerostatic spindle.
Plasma figuring technologies have been widely used in the processing of silicon-based materials at atmospheric pressure. Previous plasma figuring of silicon based optical surfaces has been undertaken using a radio frequency plasma jet through an Inductively Coupled Plasma (ICP) torch. Microwave plasma is suitable for processing those materials that cannot bear high temperature from the thermal plasma jet. For crystalline quartz (SiO4) processing, microwave plasma systems employ electrodes to couple the microwaves into the gas; however, the presence of reactive plasma interactions with any electrode surfaces, typically results in electrode degradation. To avoid this degradation, the Surface Wave Launched Microwave Induced Plasma (SWL-MIP) torch design was selected that uses the principal of surface wave launching. The electromagnetic frequency was set to 2.5 GHz for all the experiments. Argon is used as a main carrier gas. Carbon tetrafluoride (CF4) is used as a secondary gas for the creation of reactive species and consequently enables the material removal of silicon atoms from the substrates. Optical Emission Spectroscopy (OES) characterization confirmed that these parameters led to a plasma jet, which was stable both spatially and temporally. The optimum parameters were used for the material removal experiments of crystal quartz. Finally, a material removal rate of 0.18 mm 3 /min was achieved with substrate preheating to 200 ºC. The maximum surface roughness at the bottom of a measured trench increased from an Sq of 1.5 nm up to a mean average Sq of 3.5 nm.
During preliminary tea processing, moisture content is an important consideration affecting the tea quality. Traditionally, the moisture content of tea leaves was manually controlled by the joint action of multiple processing units, and maintaining stability was difficult. In this paper, a multi-unit collaborative strategy was proposed for controlling moisture content in preliminary tea processing. Multivariate methods including polynomial regression, radical basis function neural network (RBFNN), and least squares support vector machine (LSSVM) were used to establish models for moisture content prediction in the first fixation, second fixation, and drying units, with minimal root mean square errors (RMSEs) of 1.34%, 0.86%, and 0.13%, respectively. The combination of RBFNN and LSSVM, with a RMSE of 0.03%, was used to model the preliminary processing of whole tea. Rough set data mining technology was used to obtain the optimum ranges of moisture content and critical process parameters. Finally, a Monte Carlo simulation experiment was carried out within the optimum range, and moisture content design spaces for the single unit and the whole processing line were obtained. With the proposed approach, the stability of the final moisture content of tea can be improved, which is of great significance for improving tea quality and accelerating the automation of tea production.
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