Kinematic Optimization of a Redundantly Actuated Parallel Mechanism for Maximizing Stiffness and Workspace Using Taguchi Method-Reference-Cited by-同舟云学术

Kinematic Optimization of a Redundantly Actuated Parallel Mechanism for Maximizing Stiffness and Workspace Using Taguchi Method

Published:2010-10-11 Issue:1 Volume:6 Page:
ISSN:1555-1415
Container-title:Journal of Computational and Nonlinear Dynamics
language:en
Short-container-title:

Author:

Shin Hyunpyo¹,Lee Sungchul²,In Woosung¹,Jeong Jay I.³,Kim Jongwon⁴

Affiliation:

1. Graduate School of Mechanical and Aerospace Engineering, Seoul National University, Seoul 151-742, South Korea

2. Korean Institute of Machinery and Materials, Daejeon 305-343, South Korea

3. School of Mechanical and Automotive Engineering, Kookmin University, 861-1, Jeongnung-Dong, Seungbuk-Gu, Seoul, South Korea

4. School of Mechanical and Aerospace Engineering, Seoul National University, Seoul, South Korea

Abstract

We present an optimization procedure that uses the Taguchi method to maximize the mean stiffness and workspace of a redundantly actuated parallel mechanism at the same time. The Taguchi method is used to separate the more influential and controllable variables from the less influential ones among kinematic parameters in workspace analysis and stiffness analysis. In the first stage of optimization, the number of experimental variables is reduced by the response analysis. Quasi-optimal kinematic parameter group is obtained in the second stage of optimization after the response analysis. As a validation of the suggested procedure, the kinematic parameters of a planar 2-DOF parallel manipulator are optimized, which optimization procedure is used to investigate the optimal kinematic parameter groups between the length of the link and the stiffness.

Publisher

ASME International

Subject

Applied Mathematics,Mechanical Engineering,Control and Systems Engineering,Applied Mathematics,Mechanical Engineering,Control and Systems Engineering

Link

http://asmedigitalcollection.asme.org/computationalnonlinear/article-pdf/doi/10.1115/1.4002268/5776292/011017_1.pdf

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