2017
DOI: 10.1016/j.mechmachtheory.2017.05.018
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Defect-free optimal synthesis of crank-rocker linkage using nature-inspired optimization algorithms

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Cited by 76 publications
(38 citation statements)
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“…The goal function is to minimize the error between the desired and derived trajectory. Euclidian distance error between derived (P d ) and desired (P desired ) is computed to minimize the position error [27][28][29][30][31].…”
Section: Desired and Derived Trajectories Of Four-bar Mechanismmentioning
confidence: 99%
“…The goal function is to minimize the error between the desired and derived trajectory. Euclidian distance error between derived (P d ) and desired (P desired ) is computed to minimize the position error [27][28][29][30][31].…”
Section: Desired and Derived Trajectories Of Four-bar Mechanismmentioning
confidence: 99%
“…Bulatović et al (2016) developed a variable controlled deviations method and modified Krill Herd (MKH) algorithm to synthesize four-bar linkages for accomplishing approximately rectilinear motion (Bulatović and Dordević, 2009;Bulatović et al, 2016). Singh et al (2017) used nature inspired optimization algorithms to reduce the computation and get the crank-rocker mechanisms without defects (Singh et al, 2017). Sleesongsoma and Bureerat (2017) proposed a variant of teachinglearning-basedoptimization for four-bar linkage path generation, which was significantly superior to its original version (Sleesongsoma and Bureerat, 2017).…”
Section: Introductionmentioning
confidence: 99%
“…Wang et al (2019) developed a program package based on Matlab for the synthesis calculation of planar 4R linkage based on the theory of planar analytic geometry (Wang et al, 2019). Ramanpreet et al proposed a refinement scheme for the optimal syntheses of the planar crank-rocker linkage free from all defects, which is used in human knee exoskeleton (Singh et al, 2017). Bulatović and Dordević (2009) proposed the variable controlled deviations method to synthesize planar four-bar mechanisms for accomplishing approximately rectilinear motion.…”
Section: Introductionmentioning
confidence: 99%
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“…The kriging-based genetic algorithm is applied to aerodynamic design problems to reduce the number of design variables by eliminating those that have small effect on the objective function [6,7]. An algorithm based on teaching-learning-based optimization (TLBO) is presented to solve this highly nonlinear optimization problem in the 2 Mathematical Problems in Engineering crank-rocker mechanism system [8,9]. The shape optimization for path synthesis of crank-rocker mechanisms using a wavelet based on the neural network was proposed to provide an approximate solution of the synthesis problem [10,11].…”
Section: Introductionmentioning
confidence: 99%