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Genetic and hybrid algorithms for optimization of non-singular 3PRR planar parallel kinematics mechanism for machining application

Published online by Cambridge University Press:  22 February 2018

Abdur Rosyid ,
Bashar El-Khasawneh  and
Anas Alazzam
Abdur Rosyid
Affiliation:
Mechanical Engineering Department, Khalifa University of Science and Technology, P.O. Box 127788, Abu Dhabi, United Arab Emirates. E-mails: abdur.patrum@kustar.ac.ae, anas.alazzam@kustar.ac.ae
Bashar El-Khasawneh*
Affiliation:
Mechanical Engineering Department, Khalifa University of Science and Technology, P.O. Box 127788, Abu Dhabi, United Arab Emirates. E-mails: abdur.patrum@kustar.ac.ae, anas.alazzam@kustar.ac.ae
Anas Alazzam
Affiliation:
Mechanical Engineering Department, Khalifa University of Science and Technology, P.O. Box 127788, Abu Dhabi, United Arab Emirates. E-mails: abdur.patrum@kustar.ac.ae, anas.alazzam@kustar.ac.ae
*
*Corresponding author. E-mail: bashar.khasawneh@kustar.ac.ae
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Summary

This paper proposes a special non-symmetric topology of a 3PRR planar parallel kinematics mechanism, which naturally avoids singularity within the workspace and can be utilized for hybrid kinematics machine tools. Subsequently, single-objective and multi-objective optimizations are conducted to improve the performance. The workspace area and minimum eigenvalue, as well as the condition number of the homogenized Cartesian stiffness matrix across the workspace, have been chosen as the objectives in the optimization based on their relevance to the machining application. The single-objective optimization is conducted by using a single-objective genetic algorithm and a hybrid algorithm, whereas the multi-objective optimization is conducted by using a multi-objective genetic algorithm, a weighted sum single-objective genetic algorithm, and a weighted sum hybrid algorithm. It is shown that the single-objective optimization gives superior value in the optimized objective, while sacrificing the other objectives, whereas the multi-objective optimization compromises the improvement of all objectives by providing non-dominated values. In terms of the algorithms, it is shown that a hybrid algorithm can either verify or refine the optimal value obtained by a genetic algorithm.

Keywords

Genetic algorithm optimizationHybrid optimizationMulti-objective optimizationParallel manipulatorsManufacturing
Type
Articles
Information
Robotica , Volume 36 , Issue 6 , June 2018 , pp. 839 - 864
Copyright
Copyright © Cambridge University Press 2018 

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