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Optimal Design of a Compliant Microgripper for Assemble System of Cell Phone Vibration Motor Using a Hybrid Approach of ANFIS and Jaya

  • Research Article - Mechanical Engineering
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Abstract

Compliant microgripper is an important manipulation to grip a shaft into a core of a cell phone vibration motor. This paper proposes a design optimization for a new compliant microgripper. The displacement and resonant frequency of compliant microgripper are the most important characteristics, which are considered as objective functions. The length and thickness of flexure hinges are design variables. The optimal process is performed by using the hybridization algorithm between adaptive neuro-fuzzy inference system (ANFIS) and Jaya algorithm. Firstly, the data are collected by the Taguchi method. Subsequently, the signal-to-noise ratios are calculated and the weight factor of each cost function is defined by established equations well. Hereafter, a parametric control diagram is further developed by using ANFIS so as to establish the relationships between the design parameters and responses. Finally, Jaya algorithm is used to solve the multi-objective optimization problem. The results indicated that the optimal displacement and frequency were about 3260 \(\upmu \)m and 61.9 Hz, respectively. These values are satisfied with the actual requirements of assembly industry for cell phone vibration motor. The proposed hybrid optimization algorithm is also robust and effective for the compliant microgripper as compared to previous methods.

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References

  1. Sensorwiki: Eccentric Rotating Mass (ERM) Motor, pp. 1–8 (2016). http://sensorwiki.org/doku.php/actuators/eccentric_rotating_mass_erm_motor

  2. Dameitry, A.; Tsukagoshi, H.: Lightweight pneumatic semi-universal hand with two fingers aimed for a wide range of grasping. Adv. Robot. (2017). https://doi.org/10.1080/01691864.2017.1392346

  3. Han, K.; Lee, S.H.; Moon, W.; Park, J.S.; Moon, C.W.: Design and fabrication of the micro-gripper for manipulating the cell. Integr. Ferroelectr. 89, 77–86 (2007). https://doi.org/10.1080/10584580601077591

    Article  Google Scholar 

  4. Howell, L.L.: Compliant Mechanisms, Wiley-Interscience (2011)

  5. Power, M.; Seneci, C.A.; Thompson, A.J.; Yang, G.Z.: Modelling & characterization of a compliant tethered microgripper for microsurgical applications. In: International Conference on Manipulation, Automation and Robotics at Small Scales (MARSS 2017)—Proceedings (2017). https://doi.org/10.1109/MARSS.2017.8001946

  6. Gaafar, E.; Zarog, M.: A low-stress and low temperature gradient microgripper for biomedical applications. Microsyst. Technol. 23, 5415–5422 (2017). https://doi.org/10.1007/s00542-017-3325-9

    Article  Google Scholar 

  7. Niaki, M.H.; Nikoobin, A.: Design and fabrication a long-gripping-range microgripper with active and passive actuators. Iran. J. Sci. Technol. Trans. Mech. Eng. (2017). https://doi.org/10.1007/s40997-017-0135-8

  8. Liang, C.; Wang, F.; Shi, B.; Huo, Z.; Zhou, K.; Tian, Y.; Zhang, D.: Design and control of a novel asymmetrical piezoelectric actuated microgripper for micromanipulation. Sens. Actuators A Phys. 269, 227–237 (2018). https://doi.org/10.1016/j.sna.2017.11.027

    Article  Google Scholar 

  9. Somà, A.; Iamoni, S.; Voicu, R.; Müller, R.: Design and experimental testing of an electro thermal microgripper for cell manipulation. Microsyst. Technol. (2017). https://doi.org/10.1007/s00542-017-3460-3

    Google Scholar 

  10. Voicu, R.C.; Zandi, M.Al; Müller, R.; Wang, C.: Nonlinear numerical analysis and experimental testing for an electrothermal SU-8 microgripper with reduced out-of-plane displacement. J. Phys. Conf. Ser. 922, 1–6 (2017). https://doi.org/10.1088/1742-6596/922/1/012006

    Article  Google Scholar 

  11. Wang, F.; Liang, C.; Tian, Y.; Zhao, X.; Zhang, D.: Design and control of a compliant microgripper with a large amplification ratio for high-speed micro manipulation. EEE/ASME Trans. Mechatron. 21(3), 1262–1271 (2016). https://doi.org/10.1109/TMECH.2016.2523564

    Article  Google Scholar 

  12. Liang, C.; Wang, F.; Tian, Y.; Zhao, X.; Zhang, H.: A novel monolithic piezoelectric actuated flexure-mechanism based wire clamp for microelectronic device packaging. Rev. Sci. Instrum. 86(4), 045106, 1–10 (2015). https://doi.org/10.1063/1.4918621

  13. Liang, C.; Wang, F.; Tian, Y.; Zhao, X.; Zhang, D.: Grasping force hysteresis compensation of a piezoelectric-actuated wire clamp with a modified inverse Prandtl–Ishlinskii model. Rev. Sci. Instrum. 88(11), 115101, 1–10 (2017). https://doi.org/10.1063/1.5009183

  14. Jain, R.K.; Majumder, S.; Ghosh, B.; Saha, S.: Analysis of multiple robotic assemblies by cooperation of multimobile micromanipulation systems (M4S). Int. J. Adv. Manuf. Technol. 91, 3033–3050 (2017). https://doi.org/10.1007/s00170-016-9969-2

    Article  Google Scholar 

  15. Deaconescu, T.; Deaconescu, A.: Pneumatic muscle-actuated adjustable compliant gripper system for assembly operations. Stroj. Vestnik/Journal Mech. Eng. 63, 225–234 (2017). https://doi.org/10.5545/sv-jme.2016.4239

    Article  Google Scholar 

  16. Helal, M.; Alshennawy, A.A.; Alogla, A.: Optimal design of a positioning flexible hinge compliant micro-gripper mechanism with parallel movement arms. Int. J. Eng. Res. Technol. 10, 105–128 (2017)

    Google Scholar 

  17. Wang, F.; Liang, C.; Tian, Y.; Zhao, X.; Zhang, D.: Design of a piezoelectric-actuated microgripper with a three-stage flexure-based amplification. IEEE/ASME Trans. Mechatron. 20(5), 2205–2213 (2015). https://doi.org/10.1109/TMECH.2014.2368789

    Article  Google Scholar 

  18. Dao, T.P.; et al.: Analysis and optimization of a micro displacement sensor for compliant microgripper. Microsyst. Technol. 23(12), 5375–5395 (2017). https://doi.org/10.1007/s00542-017-3378-9

    Article  Google Scholar 

  19. Ho, N.L.; Dao, T.; Huang, S.; Le, H.G.: Design and optimization for a compliant gripper with force regulation mechanism. Int. J. Mech. Aerospace Ind. Mechatronic Manuf. Eng. 10(12), 1913–1919 (2016)

    Google Scholar 

  20. Huang, S.-C.; Lee, C.-M.: Optimal design of microgripper. In: First International Conference on Innovative Computing, Information and Control 2006 (ICICIC’06), vol. 3, pp. 153–156 (2006). https://doi.org/10.1109/ICICIC.2006.491

  21. Datta, R.; Pradhan, S.; Bhattacharya, B.: Analysis and design optimization of a robotic gripper using multiobjective genetic algorithm. IEEE Trans. Syst. Man Cybern. Syst. 46, 16–26 (2016). https://doi.org/10.1109/TSMC.2015.2437847

    Article  Google Scholar 

  22. Ruiz, D.; Sigmund, O.: Optimal design of robust piezoelectric microgrippers undergoing large displacements. Struct. Multidiscip. Optim. 57, 71–82 (2018). https://doi.org/10.1007/s00158-017-1863-5

    Article  MathSciNet  Google Scholar 

  23. Dao, T.P.; Huang, S.C.; Le, N.L.: Robust parameter design for a compliant microgripper based on hybrid Taguchi-differential evolution algorithm. Microsyst. Technol. 24(3), 1461–1477 (2018). https://doi.org/10.1007/s00542-017-3534-2

    Article  Google Scholar 

  24. Wu, Z.; Li, Y.: Optimal design and comparative analysis of a novel microgripper based on matrix method. In: 2014 IEEE/ASME Conference on Intelligent Mechatronics, pp. 955–960 (2014)

  25. Xiao, S.; Li, Y.; Zhao, X.: Optimal design of a novel micro-gripper with completely parallel movement of gripping arms. In: IEEE Conference on Robotics and Automation Mechatronics, RAM—Proceedings, pp. 35–40 (2011). https://doi.org/10.1109/RAMECH.2011.6070452

  26. Al, M.T.; et al.: The use of genetic algorithms in response surface methodology. Qual. Technol. Quant. Manag. 6, 295–307 (2009). https://doi.org/10.1080/16843703.2009.11673201

    Article  MathSciNet  Google Scholar 

  27. Riadh, B.; Henia, A.; Hedi, B.: Application of response surface analysis and genetic algorithm for the optimization of single point incremental forming process. Key Eng. Mater. 557, 1265–1272 (2013). https://doi.org/10.4028/www.scientific.net/KEM.554-557.1265

    Google Scholar 

  28. Suraj, S.R.K.; Ghosh, S.: Jaya based ANFIS for monitoring of two class motor imagery task. IEEE Access. 4, 9273–9282 (2016). https://doi.org/10.1109/ACCESS.2016.2637401

    Article  Google Scholar 

  29. Li, W.T.; Shi, X.W.; Hei, Y.Q.; Liu, S.F.; Zhu, J.: A hybrid optimization algorithm and its application for conformal array pattern synthesis. IEEE Trans. Antennas Propag. 58(10), 3401–3406 (2010). https://doi.org/10.1109/TAP.2010.2050425

    Article  Google Scholar 

  30. Zukhri, Z.; Paputungan, I.V.: A hybrid optimization algorithm based on genetic algorithm and ant colony optimization. Int. J. Artif. Intell. Appl. 63(4), 63–75 (2013)

    Google Scholar 

  31. Rao, R.V.; Waghmare, G.G.: A new optimization algorithm for solving complex constrained design optimization problems. Eng. Optim. 49(1), 60–83 (2017). https://doi.org/10.1080/0305215X.2016.1164855

    Article  Google Scholar 

  32. Rao, R.V.; Saroj, A.: A self-adaptive multi-population based Jaya algorithm for engineering optimization. Swarm Evol. Comput. 37, 1–26 (2017). https://doi.org/10.1016/j.swevo.2017.04.008

    Article  Google Scholar 

  33. Rao, R.V.; Rai, D.P.: Optimization of submerged arc welding process parameters using quasi-oppositional based Jaya algorithm. J. Mech. Sci. Technol. 31, 2513–2522 (2017). https://doi.org/10.1007/s12206-017-0449-x

    Article  Google Scholar 

  34. Sun, X.; Chen, W.; Tian, Y.; Fatikow, S.; Zhou, R.; Zhang, J.; Mikczinski, M.: A novel flexure-based microgripper with double amplification mechanisms for micro/nano manipulation. Rev. Sci. Instrum. 85002, 1–10 (2013)

    Google Scholar 

  35. Hao, G.; Riza, M.: Feasibility study of a gripper with thermally controlled stiffness of compliant jaws. Appl. Sci. 6(11), 367 (2016). https://doi.org/10.3390/app6110367

    Article  Google Scholar 

  36. Shi, Q.; Yu, Z.; Wang, H.; Sun, T.; Huang, Q.; Fukuda, T.; Fellow, L.: Development of a highly compact microgripper capable of online calibration for multi-sized microobject manipulation. IEEE Trans. Nanotechnol. (2018. https://doi.org/10.1109/TNANO.2018.2793883

  37. Zhang, S.; Chen, G.: Design of compliant bistable mechanism for rear trunk lid of cars. In: Proceedings of International Conference on Intelligent Robotics and Applications, pp. 291–299 (2011)

  38. Dao, T.P.; Huang, S.C.; Pham, T.T.: Hybrid Taguchi–Cuckoo search algorithm for optimization of a compliant focus positioning platform. Appl. Soft Comput. 57, 526–538 (2017). https://doi.org/10.1016/j.asoc.2017.04.038

    Article  Google Scholar 

  39. Huang, S.C.; Dao, T.P.: Design and computational optimization of a flexure-based XY positioning platform using FEA-based response surface methodology. Int. J. Precis. Eng. Manuf. 17, 1035–1048 (2016). https://doi.org/10.1007/s12541-016-0126-5

    Article  Google Scholar 

  40. Jang, J.R.: ANFIS: Adaptive-Network-based Fuzzy Inference System, IEEE transactions on systems, man, and cybernetics23.3, pp. 665–685 (1993)

  41. Zaki, A.M.; Soliman, A.M.: High Performance Robotic Gripper Based on Choice of Feedback Variables, 2010 IEEE International Conference on Information and Automation (ICIA), pp. 54–59 (2010)

  42. Dao, T.P.: Multiresponse optimization of a compliant guiding mechanism using hybrid taguchi-grey based fuzzy logic approach. Math. Probl. Eng. 2016, 1–16 (2016). https://doi.org/10.1155/2016/5386893

    Article  Google Scholar 

  43. Dao, T.P.; Huang, S.C.: Design and multi-objective optimization for a broad self-amplified 2-DOF monolithic mechanism. Sadhana (2017). https://doi.org/10.1007/s12046-017-0714-9

  44. Nguyen, S.D.; Nguyen, Q.H.; Seo, T.I.: ANFIS deriving from jointed input-output data space and applying in smart-damper identification. Appl. Soft Comput. J. 53, 45–60 (2017). https://doi.org/10.1016/j.asoc.2016.11.016

    Article  Google Scholar 

  45. Nguyen, S.D.; Seo, T.-I.: Establishing ANFIS and the use for predicting sliding control of active railway suspension systems subjected to uncertainties and disturbances. Int. J. Mach. Learn. Cybern. (2016). https://doi.org/10.1007/s13042-016-0614-z

    Google Scholar 

  46. Yu, D.; Hong, J.; Zhang, J.; Niu, Q.: Multi-objective individualized-instruction teaching-learning-based optimization algorithm. Appl. Soft Comput. J. https://doi.org/10.1016/j.asoc.2017.08.056

  47. Zhu, Y.; Liang, J.; Chen, J.; Ming, Z.: PT US CR. Knowl. Based Syst. https://doi.org/10.1016/j.knosys.2016.10.030

  48. Chau, N.L.; Dang, V.A.; Le, H.G.; Dao, T.P.: Robust parameter design and analysis of a leaf compliant joint for micropositioning systems. Arab. J. Sci. Eng. 42(11), 4811–4823 (2017). https://doi.org/10.1007/s13369-017-2682-0

    Article  Google Scholar 

  49. Jia, Y.; Zhang, X.; Xu, Q.: Design and optimization of a dual-axis pzt actuation gripper. In: Proceedings of IEEE International Conference on Robotics and Biomimetics (ROBIO), pp. 321–325 (2014)

  50. ANSYS, I.: ANSYS Workbench. Release 16

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Acknowledgements

The authors are thankful for the financial support from the HCMC University of Technology and Education, Vietnam, under Grant No. T2018-16TÐ.

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Authors and Affiliations

  1. Faculty of Mechanical Engineering, Ho Chi Minh University City of Technology and Education, Ho Chi Minh City, Vietnam

    Nhat Linh Ho & Hieu Giang Le

  2. Division of Computational Mechatronics, Institute for Computational Science, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Thanh-Phong Dao

  3. Faculty of Electrical and Electronics Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Thanh-Phong Dao

  4. Faculty of Mechanical Engineering, Industrial University of Ho Chi Minh City, Ho Chi Minh City, Vietnam

    Ngoc Le Chau

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Ho, N.L., Dao, TP., Le, H.G. et al. Optimal Design of a Compliant Microgripper for Assemble System of Cell Phone Vibration Motor Using a Hybrid Approach of ANFIS and Jaya. Arab J Sci Eng 44, 1205–1220 (2019). https://doi.org/10.1007/s13369-018-3445-2

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