Hongzhan Lv

The rotary vector reducer presents high precision and load capacity characteristics. The shape of the cycloidal gear tooth profile in the rotary vector reducer significantly affects its performance. Meanwhile, the effect of the machining error on the error between the designed and theoretical tooth profiles cannot be ignored. Thus, this paper analyzes the machining error items that affect the shape of the cycloidal gear profile from the machining process perspective. Due to the random characteristics and different distribution rules inherent in various machining errors, this paper proposes a cycloidal gear machining error compensation and modification model based on the Monte Carlo simulation method, providing a new theoretical method for compensating cycloidal gear machining errors. While compensating for machining errors, considering the impact of cycloidal gear modification on the carrying capacity, the NSGA-Ⅱoptimization algorithm is utilized to optimize the cycloidal gear modification parameter and finally, to solve the modification parameters with a more comprehensive performance.

Magnetorheological dampers are highly nonlinear damping elements which exhibit multi-dimensional data correspondence problems in the current input and damping force output data sets. Therefore, it is inconvenient to use the traditional modeling method to predict the output damping force of the magnetorheological damper accurately. Although the neural network can predict data-driven output results to a high precision, more models, parameters, and complex structures are required to predict the expected current from the magnetorheological test data set. In this study, a prediction model of the expected current in a magnetorheological damping system was proposed by using the k-nearest neighbor algorithm based on the classification algorithm in machine learning. Then, the k-nearest neighbor prediction model was trained and tested with data obtained experimentally. In order to verify the performance of the k-nearest neighbor algorithm, a comparison was made with a prediction model based on the BP neural network, and the damping force output of the predicted current was simulated. The study results show that the k-nearest neighbor prediction model established has a more straightforward working principle, fewer input variables, and a higher approximation accuracy compared with the BP neural network control system.

As a new type of intelligent damper, the magnetorheological damper has been widely used in robot, automobile NVH, and intelligent structure. However, for the intelligent response control from the structural excitation, it is the challenge to realize the intelligent control of the magnetorheological damping system. In this paper, the prediction-control mechanism of the magnetorheological damping system is modeled by a data-driven method, such as neural network and classification algorithm. The NARX (Nonlinear autoregressive with external input) neural network is used to predict the desired damping force required for the structural system in the forward direction, and the decision tree classification algorithm is used to reversely-control the desired current of the magnetorheological damping system in instant response to the structural system’s damping force requirement. The analysis results show that the prediction-control method is feasible to realize the intelligent control of the damper based on the state data of the damped system, which provides a new idea for the intelligent control of the magnetorheological damper system.

In the research on key technology of radiation-curing coil steel coating, it is necessary to evaluate the coating stability of radiation-curing coil steel coating materials. In this paper, a roll coating-simulation test equipment is proposed to test coating status. Considering the operational stability of the equipment, dynamic vibration response analysis and stability analysis of the roller-coating process are performed on the equipment. The test results show that the rotor vibration of the designed roll coating-simulation test equipment meets the requirements of rotating machinery, and the coating thickness remains stable under the vibration condition, which satisfies the working condition of the test equipment.

The dynamics of the magnetorheological damper is complex, including the inherent hysteresis characteristics and nonlinear creep behavior in the low-velocity region. Mathematical models for these complex dynamics are very important to the function of the damper. In this paper, a comparative study of the four parametric dynamic models, which are the hysteresis bi–viscous model, viscoelastic-plastic model, Bouc–Wen model, and improved Bouc–Wen model, is performed. The study includes the building of a common test apparatus and the parameter identification for the four models. The comparison of the four models concludes that (1) all four models are comparative and that (2) the improved Bouc–Wen model has the highest accuracy.

Purpose: We aim to provide a systematic methodology for the optimal design of MRD for improved damping capacity and dynamical adjustability in performing its damping function. Methods: A modified Bingham model is employed to model and simulate the MRD considering the MR fluid’s compressibility. The parameters that describe the structure of MRD and the property of the fluid are systematically examined for their contributions to the damping capacity and dynamically adjustability. A response surface method is employed to optimize the damping force and dynamically adjustable coefficient for a more practical setting related to the parameters. Results: The simulation system effectively shows the hysteretic characteristics of MRDs and shows our common sense understanding that the damping gap width and yoke diameter have significant effects on the damping characteristics of MRD. By taking a typical MRD device setup, optimal design shows an increase of the damping force by 33% and an increase of the dynamically adjustable coefficient by 17%. It is also shown that the methodology is applicable to other types of MDR devices. Conclusion: The compressibility of MR fluid is one of the main reasons for the hysteretic characteristics of MRD. The proposed simulation and optimization methods can effectively improve the MRD’s damping performance in the design stage.