Department of Mechanical Engineering, College of Engineering and Islamic Architecture, Umm Al Qura University, Mecca, Saudi Arabia.
Department of Mechanical Engineering, King Saud University, Riyadh 11421, Saudi Arabia.
Comput Intell Neurosci. 2021 Dec 24;2021:4471995. doi: 10.1155/2021/4471995. eCollection 2021.
This research focuses on the synthesis of linkage parameters for a bistable compliant system (BSCS) to be widely implemented within space applications. Initially, BSCS was theoretically modeled as a crank-slider mechanism, utilizing pseudo-rigid-body model (PRBM) on stiffness coefficient ( with a maximum vertical footprint ( ) for enhancing vibration characteristics. Correlations for mechanism linkage parameters (MLPs) and responses ( ) were set up by utilizing analysis of variance for response surface (RSM) technique. RSM evaluated the impact of MLPs at individual/interacting levels on responses. Consequently, a hybrid genetic algorithm-based particle swarm/flock optimization (GA-PSO) technique was employed and optimized at multiple levels for assessing ideal MLP combinations, in order to minimize characteristics (10% + 90% of ). Finally, GA-PSO estimated the most appropriate Pareto-frontal optimum solutions (PFOS) from nondominance set and crowd/flocking space approaches. The resulting PFOS from validation trials demonstrated significant improvement in responses. The adapted GA-PSO algorithm was executed with ease, extending the convergence period (through GA) and exhibiting a good diversity of objectives, allowing the development of large-scale statistics for all MLP permutations as optimal solutions. A vast set of optimal solutions can be used as a reference manual for mechanism developers.
本研究专注于双稳态柔顺系统（BSCS）的连接参数合成，以便在空间应用中广泛实施。最初，BSCS 被理论上建模为曲柄滑块机构，利用伪刚体模型（PRBM）对刚度系数（采用最大垂直足迹（ ）进行优化，以增强振动特性。通过响应面（RSM）技术的方差分析，建立了机构连接参数（MLP）和响应（ ）的相关性。RSM 评估了 MLP 在个体/相互作用水平上对响应的影响。因此，采用了基于混合遗传算法的粒子群/ flock 优化（GA-PSO）技术，并在多个水平上进行了优化，以评估理想的 MLP 组合，从而最小化特性（10%+90%的 ）。最后，GA-PSO 通过非支配集和群/ flock 空间方法从非支配集中估计最合适的 Pareto 前沿最优解（PFOS）。验证试验的结果 PFOS 表明响应有显著改善。经过适应性调整的 GA-PSO 算法易于执行，延长了收敛周期（通过 GA），并表现出良好的目标多样性，允许对所有 MLP 排列进行大规模统计，作为最优解。大量的最优解可以作为机构开发人员的参考手册。
