Abstract
Segmented Active Constrained Layer Damping (SACLD) is an intelligent vibration-damping structure, which could be applied to the sectors of aviation, aerospace, and transportation engineering to reduce the vibration of flexible structures. Moreover, machine learning technology is widely used in the engineering field because of its efficient multi-objective optimization. The dynamic simulation of a rotational segmental flexible manipulator system is presented, in which enhanced active constrained layer damping is carried out, and the neural network model of Genetic Algorithm-Back Propagation (GA-BP) algorithm is investigated. Vibration suppression and structural optimization of the SACLD manipulator model are studied based on vibration mode and damping prediction. The modal responses of the SACLD manipulator model at rest and rotation are obtained. In addition, the four model indices are optimized using the GA-BP neural network: axial incision size, axial incision position, circumferential incision size, and circumferential incision position. Finally, the best model for vibration suppression is obtained.
Published Version
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