Modal frequencies are widely used for vibration‐based structural health monitoring (SHM) and for capturing the dynamics of a monitored structure to reveal possible failures. However, changing environmental and operational conditions (i.e., temperature, humidity, wind load, and traffic load) may submerge the modal variability induced by structural damage, thereby falsely identifying damage of interest. This paper presents a comprehensive summary review of SHM for the prediction of modal frequency and the elimination of environment‐induced masking effects based on the data normalization method. The influence mechanisms of external variations on modal frequencies extensively reported in the literature are first described. Next, the research progress in predicting and eliminating the operational modal variability is reviewed emphatically; this progress can be primarily divided into an input–output method that focuses on the establishment of the relationship model between structural frequency and environmental conditions and an output‐only method that separates the embedded environmental variable‐induced changes depending on whether the environmental measurements are measured. Finally, the conclusions and future studies are summarized and discussed. As an overview, the major contribution of this paper is to provide objective technical references for engineers and owners and to further evaluate structural safety conditions more effectively and in a timely manner.

Eliminating environmental and operational effects on structural modal frequency: A comprehensive review

There are three motion stages for an industrial robot manipulator, including the acceleration stage, the constant velocity stage, and the deceleration stage. Aiming at reducing the residual vibration of the manipulator after the movement of the deceleration, a new method is proposed by configuring the movement parameters of the flexible manipulator. Firstly, we conduct experiments to verify a numerical vibration model of the manipulator, and then, we analyze the vibration suppression effect under different conditions based on the numerical model. The results show that in the range of one movement, the residual vibration can be well suppressed when the acceleration and deceleration time are set as a positive integer to the natural period of the manipulator operation; otherwise, the vibration suppression effect is not obvious and proportional to the difference between the acceleration/deceleration time and the manipulator natural period.

https://journals.sagepub.com/doi/pdf/10.1177/14613484221094982

An active control method for vibration reduction of a single-link flexible manipulator

Automatic riveting systems play a crucial role in the field of aircraft manufacturing. In the riveting process, the machine tool bears a large axial squeezing force, and the resulting deformation will inevitably affect the riveting quality. In this paper, a dual-machine riveting system is developed first, the kinematic chain model and the lower-numbered body of the system structure are constructed sequentially. Then, considering the interaction and coupling effect of the two machines in the actual riveting process, the relative stiffnesses of the dual machine in the resisting state are identified by loading tests. Based on the stiffness data at a combination of postures within the workspace, a Kriging prediction model is established to describe the relationship between stiffness and postures. According to the prediction results, the influence of rotational and translational axes on the spatial stiffness distribution of the riveting system is revealed. Finally, the online deformation compensation is realized by modifying the displacement of the feed axis on both sides. A riveting experiment is carried out, and the results demonstrate that the riveting quality is significantly improved after compensation.

Deformation prediction and compensation of a dual-machine riveting system for aircraft assembly

High-precision assembly conditions tend to necessitate consideration of the vibration modes of industrial robots. The modal characteristics of complex systems such as industrial robots are highly nonlinear. It means that mechanics experiments and finite element methods (FEM) to evaluate such features are usually expensive. Surrogate models combined with simulation-based design are widely used in engineering issues. However, few investigations apply surrogate models to industrial robots' modal analysis. We propose a practical scheme, i.e., the Blind-Kriging (KRG-B) based natural frequency prediction of the industrial robot, utilizing the Latin Hypercube Sampling (LHS) technique. A reliable dataset with 120 samples is generated for surrogate models based on the FEM. Then, the fourteen surrogate models with different optimization algorithms are evaluated to identify the optimal model for the natural frequency. In addition, the accuracy and robustness of the optimal surrogate model are investigated under different training samples. KRG-B model has better robustness (good fitting accuracy for both higher and lower order modes) and higher computational efficiency (1.133 s, the shortest time among all models). The proposed scheme mapping robot's joint angle and the natural frequency offers a valuable basis for further studying dynamic characteristics in industrial robotics.

Huang Zedong

Papers

Blind-Kriging based natural frequency modeling of industrial Robot