Measured load data play a crucial role in the fatigue durability analysis of mechanical structures. However, in the process of signal acquisition, time domain load signals are easily contaminated by noise. In this paper, a signal denoising method based on variational mode decomposition (VMD), wavelet threshold denoising (WTD), and singular spectrum analysis (SSA) is proposed. Firstly, a simple criterion based on mutual information entropy (MIE) is designed to select the proper mode number for VMD. Detrended fluctuation analysis (DFA) is adopted to obtain the noise level of the noisy signal, which can optimize the selection of MIE threshold. Meanwhile, the noisy signal is adaptively decomposed into band-limited intrinsic mode functions (BLIMFs) by using VMD. In addition, weighted-permutation entropy (WPE) is applied to divide the BLIMFs into signal-dominant BLIMFs and noise-dominant BLIMFs. Then, the signal-dominant BLIMFs are reconstructed with the noise-dominant BLIMFs processed by WTD. Finally, SSA is implemented for the reconstructed signal. Experimental results of synthetic signals demonstrate that the presented method outperforms the conventional digital signal denoising methods and the related methods proposed recently. Effectiveness of the proposed method is verified through experiments of the measured load signals.
Dynamic performance characterized by lower-order natural frequency and lower-order vibration mode is vital for the dynamic positioning accuracy of the ball screw feed system. This paper focuses on the evolution between the lateral vibration mode and the axial vibration mode of the slender ball screw feed system (SBSFS). Firstly, it is proved the first-order vibration mode transformation of the SBSFS is subject to the position of the nut based on an elastodynamic model. This model is established using substructure synthesis method considering the axial, torsional, and lateral deformation of the screw simultaneously through dealing with the screw as Timoshenko Beam elements. By comparing with the experimental results, the validity and accuracy of the established dynamic model are verified. The effects of the stiffness of the kinematic joints on the dynamic performance of SBSFS are then analyzed. The results demonstrate that the dynamic behavior evolution of the SBSFS undergoes the coupling effects from the kinematics joints’ stiffness. Furthermore, a dynamic index ( RSP) is proposed considering the evolution of the first-order vibration mode. On the basis of RSP, three optimization schemes are illustrated. The results show that the RSP can be used to evaluate the dynamic performance of the SBSFS considering the vibration mode.
In machine tools, the trajectory error of the tool center point (TCP) is caused due to the dynamic positioning error of feed drive systems according to the relationship in spatial structure and motion. The dynamic positioning error of the feed drive system includes axial dynamic error and lateral dynamic error. To analyze the influence of the lateral dynamic error on the trajectory error of the TCP of multi-axis machine tools, in this study, circular and butterfly trajectories were designed as the set trajectories of a machine tool with three linear axes. In addition, the tracking error models were constructed both considering the lateral dynamic error and without considering the lateral dynamic error, and the difference between the two models was quantified. The Sobol method was then used to analyze the sensitivity of the lateral dynamic error of the feed drive system to the tracking error of the TCP. Finally, experiments were performed on a three-axis machine tool. The results show the maximum lateral dynamic error in the direction perpendicular to the motion direction is 0.026 mm. The lateral dynamic error detrimentally affects the tracking error and contour error of the TCP in multi-axis machine tools.
This article develops a rapid performance evaluation approach for lower mobility hybrid robot, which provides guidance for manipulator evaluation, design, and optimization. First, a general position vector model of gravity center for the lower mobility hybrid robot in the whole workspace is constructed based on a general inverse kinematic model. A performance evaluation index based on gravity-center position is then proposed, where the coordinates pointing to the supporting direction are selected as the evaluation index of the robot performance. Furthermore, the credibility of the evaluation approach is verified from a 5-DOF hybrid robot (TriMule) by comparing with the condition number and the first natural frequency. Analysis results demonstrate that the evaluation index can not only reflect the performance spatial distribution in the whole workspace but also is sensitive to the performance difference caused by mass distribution. The proposed performance evaluation approach provides a new index for the rapid design and optimization of the cantilever robot.
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