Abstract
This article develops a new optimization approach for a compliant microgripper based on a hybrid Taguchi-teaching learning-based optimization algorithm (HTLBO). The optimization problem considers three objective functions and six design variables. The Taguchi’s parameter design is used to produce an initial population for the HTLBO. The weight factor for each response is accurately determined based on the analysis of the signal to noise ratio. Three case studies are taken into account as the basic examples of the proposed algorithm. The computational speed of the proposed algorithm is faster than that of the adaptive elitist differential evolution, the particle swarm optimization, and the genetic algorithm. The results found that the optimal responses from the HTLBO are better than those from other algorithms. The results indicated that the optimal displacement is about 1924.15 µm and the optimal frequency is approximately 170.45 Hz. The simulation and experimental validations are in good agreement with the predicted results. The proposed HTLBO can be applied to solve complicated engineering optimization problems.
Similar content being viewed by others
References
Abhishek K, Rakesh Kumar V, Datta S, Mahapatra SS (2015) Parametric appraisal and optimization in machining of CFRP composites by using TLBO (teaching-learning based optimization algorithm). J Intell Manuf 28:1769–1785. https://doi.org/10.1007/s10845-015-1050-8
Aghababa MP (2015) Optimal design of fractional-order PID controller for five bar linkage robot using a new particle swarm optimization algorithm. Soft Comput 20:4055–4067. https://doi.org/10.1007/s00500-015-1741-2
An H, Chen S, Huang H (2018) Multi-objective optimization of a composite stiffened panel for hybrid design of stiffener layout and laminate stacking sequence. Struct Multidiscip Optim 57:1411–1426. https://doi.org/10.1007/s00158-018-1918-2
Andrés C, Lozano S (2006) A particle swarm optimization algorithm for part-machine grouping. Robot Comput Integr Manuf 22:468–474. https://doi.org/10.1016/j.rcim.2005.11.013
Bhattacharya A, Bhattacharjee K, Dey SH (2014) Teaching–learning-based optimization for different economic dispatch problems. Sci Iran 21:870–884
Castillo FO, Trujillo L, Melin P (2007) Multiple objective genetic algorithms for path-planning optimization. Soft Comput 11:269–279. https://doi.org/10.1007/s00500-006-0068-4
Chau NL, Dao TP et al (2018) Optimal design of a dragonfly-inspired compliant joint for camera positioning system of nanoindentation tester based on a hybrid integration of Jaya-ANFIS. Math Probl Eng 2018:1–16. https://doi.org/10.1155/2018/8546095
Chen D, Huang C (2016) Study of injection molding warpage using analytic hierarchy process and Taguchi method. Adv Technol Innov 1:46–49
Chen T, Sun L, Chen L et al (2010) A hybrid-type electrostatically driven microgripper with an integrated vacuum tool. Sens Actuators A Phys 158:320–327. https://doi.org/10.1016/j.sna.2010.01.001
Dao TP, Huang SC (2016) Design and analysis of a compliant micro-positioning platform with embedded strain gauges and viscoelastic damper. Microsyst Technol 23:441–456. https://doi.org/10.1007/s00542-016-3048-3
Dao TP, Huang SC (2017) Compliant thin-walled joint based on zygoptera nonlinear geometry. J Mech Sci Technol 31:1293–1303. https://doi.org/10.1007/s12206-017-0228-8
Dao TP, Huang SC, Chau NL (2017a) Robust parameter design for a compliant microgripper based on hybrid Taguchi-differential evolution algorithm. Microsyst Technol 24:1461–1477. https://doi.org/10.1007/s00542-017-3534-2
Dao TP, Huang SC, Thang PT (2017b) Hybrid Taguchi-cuckoo search algorithm for optimization of a compliant focus positioning platform. Appl Soft Comput J 57:526–538. https://doi.org/10.1016/j.asoc.2017.04.038
Das PK, Behera HS, Panigrahi BK (2016) A hybridization of an improved particle swarm optimization and gravitational search algorithm for multi-robot path planning. Swarm Evol Comput 28:14–28. https://doi.org/10.1016/j.swevo.2015.10.011
Du D, Vinh H, Trung V, Quyen NH (2017) Efficiency of Jaya algorithm for solving the optimization-based structural damage identification problem based on a hybrid objective function. Eng Optim 50:1233–1251. https://doi.org/10.1080/0305215X.2017.1367392
Esfandiari MJ (2018) Optimization of reinforced concrete frames subjected to historical time-history loadings using DMPSO algorithm. Struct Multidisc Optim. https://doi.org/10.1007/s00158-018-2027-y
Esfandiari MJ, Urgessa GS, Sheikholare S, Manshadi SHD (2018) Optimum design of 3D reinforced concrete frames using DMPSO algorithm. Adv Eng Softw 115:149–160. https://doi.org/10.1016/j.advengsoft.2017.09.007
Ford S, Macias G, Lumia R (2015) Single active finger IPMC microgripper. Smart Mater Struct 24:025015. https://doi.org/10.1088/0964-1726/24/2/025015
Gülcü Ş, Mahi M, Baykan ÖK, Kodaz H (2018) A parallel cooperative hybrid method based on ant colony optimization and 3-Opt algorithm for solving traveling salesman problem. Soft Comput 22:1669–1685. https://doi.org/10.1007/s00500-016-2432-3
Hajabdollahi H (2017) Comparison of stationary and rotary matrix heat exchangers using teaching-learning-based optimization algorithm. Proc Inst Mech Eng Part E J Process Mech Eng 232:493–502. https://doi.org/10.1177/0954408917719769
Ho NL, Dao T, Huang S, Le HG (2016) Design and optimization for a compliant gripper with force regulation mechanism. Int J Aerosp Mech Eng 10:1913–1919
Jain RK, Majumder S, Ghosh B, Saha S (2015) Design and manufacturing of mobile micro manipulation system with a compliant piezoelectric actuator based micro gripper. J Manuf Syst 35:76–91. https://doi.org/10.1016/j.jmsy.2014.12.001
Joshi D, Mittal ML, Kumar M (2018) A teaching–learning-based optimization algorithm for the resource-constrained project scheduling problem. Soft Comput 21:1537–1548. https://doi.org/10.1007/s00500-015-1866-3
Kim BS, Park JS, Hun Kang B, Moon C (2012) Fabrication and property analysis of a MEMS micro-gripper for robotic micro-manipulation. Robot Comput Integr Manuf 28:50–56. https://doi.org/10.1016/j.rcim.2011.06.005
Konak A, Coit DW, Smith AE (2006) Multi-objective optimization using genetic algorithms: a tutorial. Reliab Eng Syst Saf 91:992–1007. https://doi.org/10.1016/j.ress.2005.11.018
Krishnan S, Saggere L (2012) Design and development of a novel micro-clasp gripper for micromanipulation of complex-shaped objects. Sens Actuators A Phys 176:110–123. https://doi.org/10.1016/j.sna.2011.09.030
Li Z, Zhang X (2007) Multiobjective topology optimization of compliant microgripper with geometrically nonlinearity. Proc Int Conf Integr Commer Micro Nanosyst 2007A:101–107. https://doi.org/10.1115/mnc2007-21294
Linh HN, Dao TP (2018) 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. https://doi.org/10.1007/s13369-018-3445-2
Lu Q, Cui Z, Chen X (2015) Fuzzy multi-objective optimization for movement performance of deep-notch elliptical flexure hinges. Rev Sci Instrum 86:065005. https://doi.org/10.1063/1.4922914
Mackay RE, Le HR, Clark S, Williams JA (2013) Polymer micro-grippers with an integrated force sensor for biological manipulation. J Micromech Microeng 23:015005. https://doi.org/10.1088/0960-1317/23/1/015005
Nalbant M, Gökkaya H, Sur G (2007) Application of Taguchi method in the optimization of cutting parameters for surface roughness in turning. Mater Des 28:1379–1385. https://doi.org/10.1016/j.matdes.2006.01.008
Pandey A, Datta R, Bhattacharya B (2015) Topology optimization of compliant structures and mechanisms using constructive solid geometry for 2-d and 3-d applications. Soft Comput 21:1157–1179. https://doi.org/10.1007/s00500-015-1845-8
Patel V, Savsani V (2014) Optimization of a plate-fin heat exchanger design through an improved multi-objective teaching-learning based optimization (MO-ITLBO). Chem Eng Res Des 12:2371–2382. https://doi.org/10.1016/j.cherd.2014.02.005
Rao RV, Waghmare GG (2014) A comparative study of a teaching–learning-based optimization algorithm on multi-objective unconstrained and constrained functions. J King Saud Univ Comput Inf Sci 26:332–346. https://doi.org/10.1016/j.jksuci.2013.12.004
Rao RV, Savsani VJ, Vakharia DP (2011) Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. CAD Comput Aided Des 43:303–315. https://doi.org/10.1016/j.cad.2010.12.015
Rao RV, Savsani VJ, Vakharia DP (2012) Teaching–learning-based optimization: an optimization method for continuous non-linear large scale problems. Inf Sci (Ny) 183:1–15. https://doi.org/10.1016/j.ins.2011.08.006
Roy RK (2001) Design of experiments using the Taguchi approach: 16 steps to product and process improvement. Wiley, New York
Sahu BK, Pati S, Mohanty PK, Panda S (2015) Teaching-learning based optimization algorithm based fuzzy-PID controller for automatic generation control of multi-area power system. Appl Soft Comput J 27:240–249. https://doi.org/10.1016/j.asoc.2014.11.027
Saxena A (2013) A contact-aided compliant displacement-delimited gripper manipulator. J Mech Robot 5:041005. https://doi.org/10.1115/1.4024728
Sombat A et al (2018) Perspectives and experiments of hybrid particle swarm optimization and genetic algorithms to solve optimization problems. Econometrics for financial applications. ECONVN 2018. Stud Comput Intell 760:290–297. https://doi.org/10.1007/978-3-319-73150-6_23
Sonawane SA, Kulkarni ML (2018) Multi-response optimization of wire electrical discharge machining for titanium grade-5 by weighted principal component analysis. Int J Eng Technol Innov 8:133–145
Stadler W (1979) A survey of multicriteria optimization or the vector maximum problem, part I: 1776–1960. J Optim Theory Appl 29:1–52. https://doi.org/10.1007/BF00932634
Syan CS, Ramsoobag G (2018) A differential evolution optimization approach for parameters estimation of truncated and censored failure time data. Adv Technol Innov 3:185–194
Venkatakrishnan GR, Mahadevan J, Rengaraj R (2018) Differential evolution with parameter adaptation strategy to economic dispatch incorporating wind. Intell Effic Electr Syst 20:153–165. https://doi.org/10.1007/978-981-10-4852-4_14
Wu Z, Fu W, Xue R (2015) Nonlinear inertia weighted teaching–learning-based optimization for solving global optimization problem. Comput Intell Neurosci. https://doi.org/10.1155/2015/292576
Yildiz AR (2013) Optimization of multi-pass turning operations using hybrid teaching learning-based approach. Int J Adv Manuf Technol 66:1319–1326. https://doi.org/10.1007/s00170-012-4410-y
Acknowledgements
This research is funded by Vietnam National Foundation for Science and Technology Development (NAFOSTED) under grant number 107.01-2016.20.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Ho, N.L., Dao, TP., Le Chau, N. et al. Multi-objective optimization design of a compliant microgripper based on hybrid teaching learning-based optimization algorithm. Microsyst Technol 25, 2067–2083 (2019). https://doi.org/10.1007/s00542-018-4222-6
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00542-018-4222-6