机制(生物学)
遗传算法
计算机科学
算法
几何学
物理
数学
机器学习
量子力学
作者
Shahrad Samankan,Merve Acer Kalafat,Ata Arjomandi Fard,Atakan Altınkaynak
标识
DOI:10.1177/09544062251352654
摘要
This paper optimizes the geometry of a novel Delta mechanism via origami-inspired fabrication. The aim is to maximize workspace volume without increasing the mechanism’s footprint. Optimize the geometry to increase the limitation of the mechanism and experimentally test the optimized mechanism and compare the results with the original one. The Delta mechanism’s novelty altered fabrication and design parameters. A genetic algorithm, a common optimization method, was applied. Finite element analysis examined the impact of elastic joints. Experiments compared the kinematic model’s workspace trajectory between optimized and initial mechanisms. The paper reports a 35% increase in workspace volume over the initial mechanism while maintaining a constant footprint size. The optimized mechanism achieves sub-0.5 mm root mean square (RMS) accuracy in the X , Y , and Z directions, ensuring precise positioning within the calculated workspace using rigid kinematics. Despite the expanded workspace, the accuracy of reaching boundary points remains nearly constant compared to the initial model. This study covers only a specific type of parallel mechanism fabricated by the origami-inspired method. Therefore, researchers are encouraged to test and optimize different mechanisms with higher degree of freedom further. The paper discusses implications for advancing flexible mechanisms especially origami-inspired mechanisms, a widely studied type in recent research. Employing optimization techniques can enhance accuracy, reliability, and cost-effectiveness in the fabrication process for other types of mechanisms. This paper addresses the acknowledged requirement to delve into origami-inspired mechanisms, featuring distinctive design parameters and fabrication methods when compared to conventional mechanisms. The significance of this inquiry is heightened by the existing dearth of design optimization in flexible parallel mechanisms.
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