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.

Accurate and feasible mechanical constitutive models of magnetorheological (MR) fluids can facilitate the design of MR devices. Therefore, the purpose of this article is to compare which of the three models can better describe the mechanical behavior of the MR fluids, and the experiments were implemented to measure the shear stress and apparent viscosity of a representative MR fluid under varying shear rates and different applied magnetic field conditions. The obtained rheological properties of the MR fluid are then fitted to the Bingham, Casson, and Herschel–Bulkley models to test the constitutive mechanical performance and accuracy. The results demonstrate that, although the models above appropriately describe the approximate Newtonian fluid properties of the MR fluid in the absence of a magnetic field, the Bingham and Casson models are difficult to describe the nonlinear characteristics of the MR fluid in the pre-yield region under an excited magnetic field. Meanwhile, the Casson are not sensitively responsive to equation forms change and can only be suitable to describe the mechanical behavior of the MR fluids roughly. However, the Herschel–Bulkley model can closely describe the constitutive mechanical behaviors of shear stresses varying nonlinearly with the external excited magnetic field. Especially in the pre-yield region, the model can reflect the peristaltic mechanical behaviors of MR fluids. These findings could provide theoretical guidance for the design of MR devices.

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.

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.