Micro-vibrations generated by rotating machinery could influence the working performance of the precise instruments equipped in industrial facilities, aircraft, ships, etc. Isolation systems are thus essential for preventing the high-sensitive instruments from being disturbed. In this work, a 6 degrees-of-freedom (DOF) micro-vibration isolation platform is proposed based on the quasi-zero-stiffness (QZS) isolator. The structure of the isolation platform is a conventional Stewart mechanism equipped with the QZS isolators in each leg. To analyze the stiffness of the leg, the static analysis is carried out. Afterwards, the stiffness of the platform in six directions are also discussed. Moreover, the dynamical equations of the isolation platform are formulated by the Lagrange Equation. Finally, the dynamic response of the proposed model is investigated and compared with the linear case whose stiffness of the leg is linear. The results show that the newly designed QZS platform in this work has a good isolation performance, which can attenuate the external vibrations significantly within a broad frequency band. Parametric analysis shows that the structure parameters, damping and excitation amplitude have a great influence on the isolation performance of the QZS platform. Delightfully, the result of indicates that, (a) the newly designed QZS platform is able to provide an excellent isolation performance in all the six DOFs, which advances the dynamic study of multi-dimensional QZS vibration isolation; (b) The multi-dimensional vibration isolation platform is innovatively designed using geometric nonlinear technology, there is no influence of the magnetic sources to the precision instruments when compared with the previous designs; (c) the regulation mechanism of the structure parameters of the QZS isolation platform on the dynamic transmission characteristics of the system is revealed, which indicates that the platform is particularly suitable for micro-vibration isolation whose vibration magnitude and frequency are quite low.

Magnetorheological (MR) dampers, as an intelligent vibration damping device, can quickly change the damping size of the material in milliseconds. The traditional semiactive control strategy cannot give full play to the ability of the MR dampers to consume energy and reduce vibration under different currents, and it is difficult to control the MR dampers accurately. In this paper, a semiactive control strategy based on reinforcement learning (RL) is proposed, which is based on “exploring” to learn the optimal value of the MR dampers at each step of the operation, the applied current value. During damping control, the learned optimal action value for each step is input into the MR dampers so that they provide the optimal damping force to the structure. Applying this strategy to a two‐layer frame structure was found to provide more accurate control of the MR dampers, significantly improving the damping effect of the MR dampers.

The polygonal scaled boundary finite element method (PSBFEM) is a novel method integrating the standard scaled boundary finite element method (SBFEM) and the polygonal mesh technique. This work discusses developing a PSBFEM framework within the commercial finite element software Abaqus. The PSBFEM is implemented by the User Element Subroutine (UEL) feature of the software. The details on the main procedures to interact with Abaqus, defining the UEL element, and solving the stiffness matrix by the eigenvalue decomposition are present. Moreover, we also develop the preprocessing module and the postprocessing module using the Python script to generate meshes automatically and visualize results. Several benchmark problems from two-dimensional linear elastostatics are solved to validate the proposed implementation. The results show that PSBFEM-UEL has significantly better than FEM convergence and accuracy rate with mesh refinement. The implementation of PSBFEM-UEL can conveniently use arbitrary polygon elements by the polygon/quadtree discretizations in the Abaqus. The developed UEL and the associated input files can be downloaded from https://github.com/hhupde/PSBFEM-Abaqus.