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Optimal Synthesis of a Motion Generation Six-Bar Linkage
Author(s)
Phromphan, Pakin
Suvisuthikasame, Jirachot
Kaewmongkol, Metas
Chanpichitwanich, Woravech
Sleesongsom, Suwin
Date Issued
January 1, 2022
Type
Conference Paper
Abstract
This research presents a new motion generation technique combined with the latest updated of teaching-learning-based optimization with a diversity archive (ATLBO-DA), which has been proved the performance in the previous study. The six-bar prototype in this study is formulated by creating the second loop attached to the simple four-bar model, which expects to follow the Watt I model. Two specific motion generation problems are used to test the performance of this technique. The objective function is created in the form of weighted sum between the position error and the angle error to make it can perform in a single objective optimization problem. The performance of the optimizations with different weight is investigated in this research. The results show that the purposed technique performs with a good accuracy in the specific motion without prescribed timing problems.
Citation
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 13345 LNCS, 389-398, 2022