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Multiobjective optimization of parallel kinematic mechanisms by the genetic algorithms

Published online by Cambridge University Press:  30 September 2011

Ridha Kelaiaia ,
Olivier Company  and
Abdelouahab Zaatri
Ridha Kelaiaia*
Affiliation:
Department of Mechanical Engineering, Faculty of Technology, Skikda University, Algeria LIRMM UMR 5506 CNRS–UM2, 161 rue Ada, 34392 Montpellier Cedex 5, France
Olivier Company
Affiliation:
LIRMM UMR 5506 CNRS–UM2, 161 rue Ada, 34392 Montpellier Cedex 5, France
Abdelouahab Zaatri
Affiliation:
Laboratory of Advanced Technologies, University of Constantine, Algeria
*
*Corresponding author. E-mail: [email protected]
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Summary

It is well known that Parallel Kinematic Mechanisms (PKMs) have an intrinsic dynamic potential (very high speed and acceleration) with high precision and high stiffness. Nevertheless, the choice of optimal dimensions that provide the best performances remains a difficult task, since performances strongly depend on dimensions. On the other hand, there are many criteria of performance that must be taken into account for dimensional synthesis, and which are sometimes antagonist. This paper presents an approach of multiobjective optimization for PKMs that takes into account several criteria of performance simultaneously that have a direct impact on the dimensional synthesis of PKMs. We first present some criteria of performance such as the workspace, transmission speeds, stiffness, dexterity, precision, as well as dynamic dexterity. Secondly, we present the problem of dimensional synthesis, which will be defined as a multiobjective optimization problem. The method of genetic algorithms is used to solve this type of multiobjective optimization problem by means of NSGA-II and SPEA-II algorithms. Finally, based on a linear Delta architecture, we present an illustrative application of this methodology to a 3-axis machine tool in the context of manufacturing of automotive parts.

Keywords

Parallel robotsParallel kinematic machine toolCriteria of performanceMultiobjective optimizationGenetic algorithms NSGA-II and SPEA-II
Type
Articles
Information
Robotica , Volume 30 , Issue 5 , September 2012 , pp. 783 - 797
Copyright
Copyright © Cambridge University Press 2011

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