Experimental Analysis and Dynamic Optimization Design of Hinge Mechanism

Abstract

<div class="section abstract"><div class="htmlview paragraph">Optimization design of hard point parameters for hinge mechanism has been paid more attention in recent years, attributable to their significant improvement in dynamic performance. In this paper, the experimental analysis and dynamic optimization design of hinge mechanism is performed. The acceleration measurement experiments are carried out at different arrangement points and under different working conditions. Furthermore, the accuracy of established multi-body dynamics model is verified by three-axis accelerometer measurement experiment. In addition, sensitivity analysis for electric strut and gas strut coordinates is performed and shows that the Y coordinate of the lower end point of the electric strut is the design variable that has the greatest impact on the responses. To improve the dynamic performance of the hinge mechanism, a surrogate-assisted NSGA-II multi-objective optimization design framework for hard point coordinates of struts, combining the radial basis neural network (RBF), polynomial response surface model (PRS), NSGA-II is carried out to obtain the Pareto frontier. Finally, a multi-objective decision-making method inspired by TOPSIS and entropy analysis is performed to calculate the unique best from the Pareto frontier. Finally, the optimized results shown that the optimized hinge mechanism is better than the original one, i.e., the dynamic responses are improved by 25.2%, 39.8%, 39.8% and 58.8% for door mass center acceleration, peak force of rotating pair 1, peak force of rotating pair 2 and motor driving force.</div></div>