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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: [email protected], [email protected]
Bashar El-Khasawneh*
Affiliation:
Mechanical Engineering Department, Khalifa University of Science and Technology, P.O. Box 127788, Abu Dhabi, United Arab Emirates. E-mails: [email protected], [email protected]
Anas Alazzam
Affiliation:
Mechanical Engineering Department, Khalifa University of Science and Technology, P.O. Box 127788, Abu Dhabi, United Arab Emirates. E-mails: [email protected], [email protected]
*
*Corresponding author. E-mail: [email protected]
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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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References

1. El-Khasawneh, B. S. and Ferreira, P., “On using parallel link manipulators for machine tools,” Trans. NAMRI/SME 25, 305–310 (1997).Google Scholar
2. Harib, K. H., Sharif Ullah, A. M. M. and Hammami, A., “A hexapod-based machine tool with hybrid structure: Kinematic analysis and trajectory planning,” Int. J. Mach. Tools Manuf. 47, 1426–1432 (2007).Google Scholar
3. Fassi, I. and Wiens, G.J., “Multiaxis machining: PKMs and traditional machining centers,” J. Manuf. Process. 2 (1), 1–76 (2000).Google Scholar
4. Zhang, D., Parallel Robot Machine Tools (Springer, New York, 2010).Google Scholar
5. Zhang, D., Wang, L., Gao, Z. and Su, X., “On performance enhancement of parallel kinematic machine,” J. Intell. Manuf. 24, 267–276 (2013).Google Scholar
6. Wu, J., Wang, J., Li, T. and Wang, L., “Dynamic analysis of the 2-DOF planar parallel manipulator of a heavy duty hybrid machine tool,” Int. J. Adv. Manuf. Technol. 34, 413–420 (2007).Google Scholar
7. Merlet, J. P., Parallel Robots, 2nd ed. (Springer, Dordrecht, 2006).Google Scholar
8. Pham, P., Hybrid-Kinematic Mechanisms (Suedwestdeutscher Verlag fuer Hochschulschriften AG, Saarbrücken, Germany, 2009).Google Scholar
9. Assal, S. F. M., “Non-singular 3-DOF Planar Parallel Manipulator with High Orientational Capability for a Hybrid Machine Tool,” Proceedings of the 16th International Conference on Advanced Robotics (ICAR), Montevideo, Uruguay (2013).Google Scholar
10. Wang, L., Wu, J., Wang, J. and You, Z., “An experimental study of a redundantly actuated parallel manipulator for a 5-DOF hybrid machine tool,” IEEE/ASME Trans. Mechatron. 14 (1), 72–81 (2009).Google Scholar
11. Wu, J., Wang, L. and You, Z., “A new method for optimum design of parallel manipulator based on kinematics and dynamics,” Nonlinear Dyn. 61, 717–727 (2010).Google Scholar
12. Gyorgy, A. and Kocsis, L., “Efficient multi-start strategies for local search algorithms,” J. Artif. Intell. Res. 41, 407–444 (2011).Google Scholar
13. Coello, C. A. C., “An updated survey of GA-based multiobjective optimization techniques,” ACM Comput. Surv. 32 (2), 109–143 (2000).Google Scholar
14. Deb, K., Multi-Objective Optimization Using Evolutionary Algorithms (John Wiley & Sons, Inc., New York, NY, USA, 2001).Google Scholar
15. Tan, K. C., Khor, E. F. and Lee, T. H., Multiobjective Evolutionary Algorithms and Applications (Springer-Verlag, London, 2005).Google Scholar
16. Konak, A., Coit, D. W. and Smith, A. E., “Multi-objective optimization using genetic algorithms: A tutorial,” Reliab. Eng. Sys. Saf. 91, 992–1007 (2006).CrossRefGoogle Scholar
17. Coello, C. A. C., Lamont, G. B. and Van Veldhuizen, D. A., Evolutionary Algorithms for Solving Multi-Objective Problems, 2nd ed. (Springer, New York, NY, USA, 2007).Google Scholar
18. Lara-Molina, F. A., Rosario, J. M. and Dumur, D., “Multi-objective Optimization of Stewart-Gough Manipulator Using Global Indices,” Proceedings of IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM2011), Budapest, Hungary (Jul. 3–7, 2011).Google Scholar
19. Kelaiaia, R., Company, O. and Zaatri, A., “Multiobjective optimization of parallel kinematic mechanisms by the genetic algorithms,” Robotica 30 (5), 783–797 (2012).Google Scholar
20. Wu, G., “Multiobjective optimum design of a 3-RRR spherical parallel manipulator with kinematic and dynamic dexterities,” Model. Identif. Control 33 (3), 111–122 (2012).Google Scholar
21. Bounab, B., “Multi-objective optimal design based kineto-elastostatic performance for the DELTA parallel mechanism,” Robotica 34, 258–273 (2016).CrossRefGoogle Scholar
22. Gao, Z. and Zhang, D., “Performance analysis, mapping, and multiobjective optimization of a hybrid robotic machine tool,” IEEE Trans. Ind. Electron. 62 (1), 423–433 (2015).Google Scholar
23. Kelaiaia, R., Zaatri, A., Company, O. and Chikh, L., “Some investigations into the optimal dimensional synthesis of parallel robots,” Int. J. Adv. Manuf. Technol. 83, 1525–1538 (2016)CrossRefGoogle Scholar
24. Lou, Y. J., Liu, G. F. and Li, Z. X., “Randomized optimal design of parallel manipulators,” IEEE Trans. Autom. Sci. Eng. 4 (2), 625–649 (2008).Google Scholar
25. Shiakolas, P. S., Koladiya, D. and Kebrle, J., “On the optimum synthesis of six-bar linkages using differential evolution and the goemetric centroid of precision positions technique,” Mech. Mach. Theory 40 (3), 319–335 (2005).CrossRefGoogle Scholar
26. Bulatović, R. R. and Dordeević, S. R., “On the optimum synthesis of a four-bar linkage using differential evolution and method of variable controlled deviations,” Mech. Mach. Theory 44 (1), 235–246 (2009).Google Scholar
27. Xu, Q. and Li, Y., “Error analysis and optimal design of a class of translational parallel kinematic machine using particle swarm optimization,” Robotica 27 (1), 67–78 (2009).Google Scholar
28. Li, Y. and Xu, Q., “Design and analysis of a totally decoupled flexure-based XY parallel micromanipulator,” IEEE Trans. Robot. 25 (3), 645–657 (2009).Google Scholar
29. Li, Y. and Xu, Q., “A totally decoupled piezo-driven XYZ flexure parallel micropositioning stage for micro/nanomanipulation,” IEEE Trans. Autom. Sci. Eng. 8 (2), 265–279 (2011).Google Scholar
30. Abbasnejad, G., Daniali, H. M. and Fathi, A., “Architecture optimization of 4PUS+1PS parallel manipulator,” Robotica 29 (5), 683–690 (2011).CrossRefGoogle Scholar
31. Gao, Z., Zhang, D. and Ge, Y., “Design optimization of a spatial six degree-of-freedom parallel manipulator based on artificial intelligence approaches,” Robot. Comput.-Integr. Manuf. 26, 180–189 (2010).Google Scholar
32. Wu, J., Wang, J., Wang, L. and You, Z., “Performance comparison of three planar 3-DOF parallel manipulators with 4-RRR, 3-RRR and 2-RRR structures,” Mechatronics 20, 510–517 (2010).CrossRefGoogle Scholar
33. Hodgins, J. and Zhang, D., “Improvement, Optimization, and Prototyping of a Three Translational Degree of Freedom Parallel Robot,” Proceedings of the ASME 36th Mechanisms and Robotics Conference, Chicago, Illinois, USA (Aug. 12–15, 2012).CrossRefGoogle Scholar
34. Shijun, Y., A Serial-Parallel Hybrid Robot for Machining of Complex Surfaces Ph.D. Thesis (National University of Singapore, 2015).Google Scholar
35. El-Khasawneh, B. S., AlAli, A., Alazzam, A., Gan, D. and Tayeh, M. A., “A Novel 5 Degrees-of-Freedom Hybrid Serial-Parallel Kinematics Manipulator (Two-3PRR): Inverse and Forward Kinematics,” Proceedings of 2014 Workshop on Fundamental Issues and Future Research Directions for Parallel Mechanisms and Manipulators, Tianjin, China (Jul. 7–8, 2014).Google Scholar
36. El-Khasawneh, B. S., “The Kinematics and Calibration of a 5 DOF Hybrid Serial-Parallel Kinematics Manipulator,” Proceedings of the 8th International Symposium on Mechatronics and its Applications (ISMA'12) Conference, Sharjah, UAE (Apr. 10–12, 2012).Google Scholar
37. Rosyid, A., El-Khasawneh, B. S. and Alazzam, A., “Optimized Planar 3PRR Mechanism for 5 Degrees-Of-Freedom Hybrid Kinematics Manipulator,” Proceedings of the ASME 2015 International Mechanical Engineering Congress and Exposition IMECE2015, Houston, Texas, USA (Nov. 13–19, 2015).CrossRefGoogle Scholar
38. Gosselin, C. M. and Sefrioui, J., “Polynomial Solutions for the Direct Kinematic Problem of Planar Three-Degree-of-Freedom Parallel Manipulators,” Proceedings of the 5th International Conference on Advanced Robotics ‘Robots in Unstructured Environments (IEEE, 1991) pp. 1124–1124.CrossRefGoogle Scholar
39. Gosselin, C. M., Sefrioui, J. and Richard, M. J., “Solutions polynomiales au problème de la cinématique des manipulateurs parallèles plans à trois degrés de liberté,” Mech. Mach. Theory 27, 107–119 (1992).Google Scholar
40. Moré, J. J., “The Levenberg-Marquardt Algorithm: Implementation and Theory,” In: Numerical Analysis, Lecture Notes in Mathematics vol. 630, (Watson, G. A., ed.) (Springer Verlag, 1977) pp. 105–116.Google Scholar
41. Tsai, L. W., Robot Analysis: The Mechanics of Serial and Parallel Manipulators (John Wiley & Sons, Inc., New York, NY, USA, 1999) pp. 260–297.Google Scholar
42. El-Khasawneh, B. S. and Ferreira, P., “Computation of stiffness and stiffness bounds for parallel link manipulators,” Int. J. Mach. Tools Manuf. 39 (2), 321–342 (2012).Google Scholar
43. Angeles, J. and Park, F. C., “Performance Evaluation and Design Criteria,” In: Springer Handbook of Robotics, Part B. Robot Structures (Springer-Verlag, Berlin Heidelberg, 2008) pp. 229–244.Google Scholar
44. Khan, W. A. and Angeles, J., “The kinetostatic optimization of robotic manipulators: The inverse and the direct problems,” ASME J. Mech. Des. 128, 168–178 (2006).CrossRefGoogle Scholar