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Arabian Journal for Science and Engineering

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06.05.2020 | Research Article-Mechanical Engineering

A Multi-response Optimal Design of Bridge Amplification Mechanism Based on Efficient Approach of Desirability, Fuzzy Logic, ANFIS and LAPO Algorithm

verfasst von: Ngoc Le Chau, Ngoc Thoai Tran, Thanh-Phong Dao

Erschienen in: Arabian Journal for Science and Engineering | Ausgabe 7/2020

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Abstract

Compliant mechanism is becoming increasingly to be a part of precision engineering, robotics, and bioengineering thanks to excellent advantages of free friction, free lubricant, no backlash, monolithic structure, and minimal assembly. However, design and analysis of compliant mechanism has been facing challenges due to a coupling of kinematic and mechanical behaviors in comparison with rigid-body counterparts. Particularly, considering a multi-response optimization design, the problem is becoming more and more complex. Thus, this article proposes a new efficient hybrid methodology to resolve multi-objective optimization design of compliant mechanisms. A bridge amplification mechanism with three numerical examples is a case study to demonstrate the effectiveness of the proposed optimizing technique. A hybridization is developed by combining finite element method, statistical method, desirability function approach, fuzzy logic system, adaptive neuro-fuzzy inference system (ANFIS), and lightning attachment procedure optimization (LAPO). A 3D finite element model for the bridge amplification mechanism is designed, and then Box–Behnken design is employed to construct numerical experiments. The sensitivity of geometrical parameters of the mechanism is investigated through analysis of variance and Taguchi approach to make a few populations for the LAPO. Subsequently, desirability values of the displacement and safety factor of the mechanism are determined, and the results are transferred into the fuzzy logic system. The output of this system is considered as single combined objective function. By developing the ANFIS structure, the refined design variables are well mapped with the output of FIS. Finally, LAPO algorithm is adopted for solving the multi-objective optimization problem for the mechanism. The results reveal that the proposed method is more efficient than the Taguchi-based fuzzy logic. Besides, the performances of the proposed method are superior to the Jaya algorithm and TLBO algorithm through Wilcoxon signed rank test and Friedman test. The results of this article can be useful for complex optimization problems.

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Hopkins, J.B.; Culpepper, M.L.: Synthesis of precision serial flexure systems using freedom and constraint topologies (FACT). Precis. Eng. (2011). https://doi.org/10.1016/j.precisioneng.2011.04.006 CrossRef

Choi, K.; Lee, J.J.; Kim, G.H.; Lim, H.J.; Kwon, S.G.: Amplification ratio analysis of a bridge-type mechanical amplification mechanism based on a fully compliant model. Mech. Mach. Theory 121, 355–372 (2018). https://doi.org/10.1016/j.mechmachtheory.2017.11.002 CrossRef

Zhou, X.; Xu, H.; Cheng, J.; Zhao, N.; Chen, S.C.: Flexure-based Roll-to-roll Platform: a practical solution for realizing large-area microcontact printing. Sci. Rep. 5, 1–10 (2015). https://doi.org/10.1038/srep10402 CrossRef

Kim, G.W.; Kim, J.: Compliant bistable mechanism for low frequency vibration energy harvester inspired by auditory hair bundle structures. Smart Mater. Struct. (2013). https://doi.org/10.1088/0964-1726/22/1/014005 CrossRef

Sun, X.; Yang, B.: A new methodology for developing flexure-hinged displacement amplifiers with micro-vibration suppression for a giant magnetostrictive micro drive system. Sens. Actuators A: Phys. (2017). https://doi.org/10.1016/j.sna.2017.04.009 CrossRef

Liu, M.; Zhang, X.; Fatikow, S.: Design and analysis of a multi-notched flexure hinge for compliant mechanisms. Precis. Eng. 48, 292–304 (2017). https://doi.org/10.1016/j.precisioneng.2016.12.012 CrossRef

Howell, L.L.; Magleby, S.P.; Olsen, B.M.: Handbook of Compliant Mechanisms. Wiley, Hoboken (2013)CrossRef

Chen, S.; Ling, M.; Zhang, X.: Design and experiment of a millimeter-range and high-frequency compliant mechanism with two output ports. Mech. Mach. Theory 126, 201–209 (2018). https://doi.org/10.1016/j.mechmachtheory.2018.04.003 CrossRef

Teo, T.J.; Chen, I.M.; Yang, G.; Lin, W.: A generic approximation model for analyzing large nonlinear deflection of beam-based flexure joints. Precis. Eng. 34, 607–618 (2010). https://doi.org/10.1016/j.precisioneng.2010.03.003 CrossRef

10.

Parvari Rad, F.; Vertechy, R.; Berselli, G.; Parenti-Castelli, V.: Analytical compliance analysis and finite element verification of spherical flexure hinges for spatial compliant mechanisms. Mech. Mach. Theory 101, 168–180 (2016). https://doi.org/10.1016/j.mechmachtheory.2016.01.010 CrossRef

11.

Wu, J.; Zhang, Y.; Cai, S.; Cui, J.: Modeling and analysis of conical-shaped notch flexure hinges based on NURBS. Mech. Mach. Theory (2018). https://doi.org/10.1016/j.mechmachtheory.2018.07.005 CrossRef

12.

Midha, A.; Howell, L.L.; Norton, T.W.: Limit positions of compliant mechanisms using the pseudo-rigid-body model concept. Mech. Mach. Theory 35, 99–115 (2000). https://doi.org/10.1016/S0094-114X(98)00093-7 MathSciNetCrossRefMATH

13.

Howell, L.L.: Compliant Mechanisms. Wiley, Hoboken (2011)

14.

Lobontiu, N.: Compliant Mechanisms: Design of Flexure Hinges. CRC Press, Boca Raton (2002)CrossRef

15.

Koseki, Y.; Tanikawa, T.; Koyachi, N.; Arai, T.: Kinematic analysis of a translational 3-d.o.f. micro-parallel mechanism using the matrix method. Adv. Robot. 16, 251–264 (2002). https://doi.org/10.1163/156855302760121927 CrossRef

16.

Ling, M.; Cao, J.; Jiang, Z.; Lin, J.: Theoretical modeling of attenuated displacement amplification for multistage compliant mechanism and its application. Sens. Actuators A: Phys. 249, 15–22 (2016). https://doi.org/10.1016/j.sna.2016.08.011 CrossRef

17.

Ling, M.; Cao, J.; Pehrson, N.: Kinetostatic and dynamic analyses of planar compliant mechanisms via a two-port dynamic stiffness model. Precis. Eng. 57, 149–161 (2019). https://doi.org/10.1016/j.precisioneng.2019.04.004 CrossRef

18.

Smith, S.T.; Chetwynd, D.G.; Bowen, D.K.: Design and assessment of monolithic high precision translation mechanisms. J. Phys. E (1987). https://doi.org/10.1088/0022-3735/20/8/005 CrossRef

19.

Awtar, S.; Sen, S.: A generalized constraint model for two-dimensional beam flexures: nonlinear load-displacement formulation. J. Mech. Des. Trans. ASME 132, 0810081–08100811 (2010). https://doi.org/10.1115/1.4002005 CrossRef

20.

Xu, Q.; Li, Y.: Analytical modeling, optimization and testing of a compound bridge-type compliant displacement amplifier. Mech. Mach. Theory 46, 183–200 (2011). https://doi.org/10.1016/j.mechmachtheory.2010.09.007 CrossRefMATH

21.

Zhu, W.Le; Zhu, Z.; Guo, P.; Ju, B.F.: A novel hybrid actuation mechanism based XY nanopositioning stage with totally decoupled kinematics. Mech. Syst. Signal Process. 99, 747–759 (2018). https://doi.org/10.1016/j.ymssp.2017.07.010 CrossRef

22.

Zhu, B.; Zhang, X.; Wang, N.: Topology optimization of hinge-free compliant mechanisms with multiple outputs using level set method. Struct. Multidiscip. Optim. 47, 659–672 (2013). https://doi.org/10.1007/s00158-012-0841-1 MathSciNetCrossRefMATH

23.

Wang, N.; Zhang, Z.; Zhang, X.; Cui, C.: Optimization of a 2-DOF micro-positioning stage using corrugated flexure units. Mech. Mach. Theory 121, 683–696 (2018). https://doi.org/10.1016/j.mechmachtheory.2017.11.021 CrossRef

24.

Liu, M.; Zhang, X.; Fatikow, S.: Design and analysis of a high-accuracy flexure hinge. Rev. Sci. Instrum. (2016). https://doi.org/10.1063/1.4948924 CrossRef

25.

Edwards, K.L.: Compliant mechanisms. Mater. Des. (2002). https://doi.org/10.1016/s0261-3069(01)00088-7 CrossRef

26.

Dao, T.P.; Huang, S.C.: Design and multi-objective optimization for a broad self-amplified 2-DOF monolithic mechanism. Sadhana - Acad. Proc. Eng. Sci. 42, 1527–1542 (2017). https://doi.org/10.1007/s12046-017-0714-9 MathSciNetCrossRefMATH

27.

Choi, K.B.; Han, C.S.: Optimal design of a compliant mechanism with circular notch flexure hinges. Proc. Inst. Mech. Eng. Part C J. Mech. Eng. Sci. 221, 385–392 (2007). https://doi.org/10.1243/0954406JMES312 CrossRef

28.

Fossati, G.G.; Miguel, L.F.F.; Casas, W.J.P.: Multi-objective optimization of the suspension system parameters of a full vehicle model. Optim. Eng. 20, 151–177 (2019). https://doi.org/10.1007/s11081-018-9403-8 CrossRefMATH

29.

Dao, T.P.; Ho, N.L.; Nguyen, T.T.; Le, H.G.; Thang, P.T.; Pham, H.T.; Do, H.T.; Tran, M.D.; Nguyen, T.T.: Analysis and optimization of a micro-displacement sensor for compliant microgripper. Microsyst. Technol. 23, 5375–5395 (2017). https://doi.org/10.1007/s00542-017-3378-9 CrossRef

30.

Ling, M.; Cao, J.; Jiang, Z.; Lin, J.: A semi-analytical modeling method for the static and dynamic analysis of complex compliant mechanism. Precis. Eng. 52, 64–72 (2018). https://doi.org/10.1016/j.precisioneng.2017.11.008 CrossRef

31.

Tian, Y.; Shirinzadeh, B.; Zhang, D.: Closed-form compliance equations of filleted V-shaped flexure hinges for compliant mechanism design. Precis. Eng. 34, 408–418 (2010). https://doi.org/10.1016/j.precisioneng.2009.10.002 CrossRef

32.

Costa, N.R.; Lourenço, J.; Pereira, Z.L.: Desirability function approach: a review and performance evaluation in adverse conditions. Chemom. Intell. Lab. Syst. 107, 234–244 (2011). https://doi.org/10.1016/j.chemolab.2011.04.004 CrossRef

33.

Pawade, R.S.; Joshi, S.S.: Multi-objective optimization of surface roughness and cutting forces in high-speed turning of Inconel 718 using Taguchi grey relational analysis (TGRA). Int. J. Adv. Manuf. Technol. (2011). https://doi.org/10.1007/s00170-011-3183-z CrossRef

34.

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

35.

Arasu, M.V.; Arokiyaraj, S.; Viayaraghavan, P.; Kumar, T.S.J.; Duraipandiyan, V.; Al-Dhabi, N.A.; Kaviyarasu, K.: One step green synthesis of larvicidal, and azo dye degrading antibacterial nanoparticles by response surface methodology. J. Photochem. Photobiol. B Biol. 190, 154–162 (2019). https://doi.org/10.1016/j.jphotobiol.2018.11.020 CrossRef

36.

Jiang, P.; Wang, C.; Zhou, Q.; Shao, X.; Shu, L.; Li, X.: Optimization of laser welding process parameters of stainless steel 316L using FEM, Kriging and NSGA-II. Adv. Eng. Softw. 99, 147–160 (2016). https://doi.org/10.1016/j.advengsoft.2016.06.006 CrossRef

37.

Wang, B.; Moayedi, H.; Nguyen, H.; Foong, L.K.; Rashid, A.S.A.: Feasibility of a novel predictive technique based on artificial neural network optimized with particle swarm optimization estimating pullout bearing capacity of helical piles. Eng. Comput. (2019). https://doi.org/10.1007/s00366-019-00764-7 CrossRef

38.

Koopialipoor, M.; Fahimifar, A.; Ghaleini, E.N.; Momenzadeh, M.; Armaghani, D.J.: Development of a new hybrid ANN for solving a geotechnical problem related to tunnel boring machine performance. Eng. Comput. (2019). https://doi.org/10.1007/s00366-019-00701-8 CrossRef

39.

Macura, L.; Voznak, M.: Multi-criteria analysis and prediction of network incidents using monitoring system. J. Adv. Eng. Comput. 1, 29 (2017). https://doi.org/10.25073/jaec.201711.47 CrossRef

40.

Moayedi, H.; Raftari, M.; Sharifi, A.; Jusoh, W.A.W.; Rashid, A.S.A.: Optimization of ANFIS with GA and PSO estimating α ratio in driven piles. Eng. Comput. (2019). https://doi.org/10.1007/s00366-018-00694-w CrossRef

41.

Sreedhara, B.M.; Rao, M.; Mandal, S.: Application of an evolutionary technique (PSO–SVM) and ANFIS in clear-water scour depth prediction around bridge piers. Neural Comput. Appl. 6, 1–15 (2018). https://doi.org/10.1007/s00521-018-3570-6 CrossRef

42.

Le Chau, N.; Dao, T.P.; Dang, V.A.: An efficient hybrid approach of improved adaptive neural fuzzy inference system and teaching learning-based optimization for design optimization of a jet pump-based thermoacoustic-Stirling heat engine. Neural Comput. Appl. (2019). https://doi.org/10.1007/s00521-019-04249-y CrossRef

43.

Keshtiara, M.; Golabi, S.; Tarkesh Esfahani, R.: Multi-objective optimization of stainless steel 304 tube laser forming process using GA. Eng. Comput. (2019). https://doi.org/10.1007/s00366-019-00814-0 CrossRef

44.

Wang, D.; Tan, D.; Liu, L.: Particle swarm optimization algorithm: an overview. Soft Comput. (2018). https://doi.org/10.1007/s00500-016-2474-6 CrossRef

45.

Dao, T.-P.; Huang, S.-C.; Le Chau, N.: Robust parameter design for a compliant microgripper based on hybrid Taguchi-differential evolution algorithm. Microsyst. Technol. (2017). https://doi.org/10.1007/s00542-017-3534-2 CrossRef

46.

Dao, T.-P.; Huang, S.-C.; Thang, P.T.: Hybrid Taguchi-cuckoo search algorithm for optimization of a compliant focus positioning platform. Appl. Soft Comput. J. (2017). https://doi.org/10.1016/j.asoc.2017.04.038 CrossRef

47.

Senkerik, R.; Zelinka, I.; Pluhacek, M.: Chaos-based optimization—a review. J. Adv. Eng. Comput. 1, 68 (2017). https://doi.org/10.25073/jaec.201711.51 CrossRef

48.

Zatloukal, F.; Znoj, J.: Malware detection based on multiple PE headers identification and optimization for specific types of files. J. Adv. Eng. Comput. 1, 153 (2017). https://doi.org/10.25073/jaec.201712.64 CrossRef

49.

Chernogorov, I.; Polyakh, V.; Yarakhmedov, O.: Search optimization opportunities of modified self-organizing migrating algorithm in multi-extremal tasks environment. J. Adv. Eng. Comput. 1, 144 (2017). https://doi.org/10.25073/jaec.201712.60 CrossRef

50.

Dinh-Cong, D.; Pham-Duy, S.; Nguyen-Thoi, T.: Damage detection of 2D frame structures using incomplete measurements by optimization procedure and model reduction. J. Adv. Eng. Comput. 2, 164 (2018). https://doi.org/10.25073/jaec.201823.203 CrossRef

51.

Venkata Rao, R.: Review of applications of TLBO algorithm and a tutorial for beginners to solve the unconstrained and constrained optimization problems. Decis. Sci. Lett. 5, 1–30 (2016). https://doi.org/10.5267/j.dsl.2015.9.003 CrossRef

52.

Nenavath, H.; Jatoth, R.K.: Hybrid SCA–TLBO: a novel optimization algorithm for global optimization and visual tracking. Neural Comput. Appl. 6, 1–30 (2018). https://doi.org/10.1007/s00521-018-3376-6 CrossRef

53.

Rao, R.V.; Keesari, H.S.; Oclon, P.; Taler, J.: An adaptive multi-team perturbation-guiding Jaya algorithm for optimization and its applications. Eng. Comput. (2019). https://doi.org/10.1007/s00366-019-00706-3 CrossRef

54.

Nematollahi, A.F.; Rahiminejad, A.; Vahidi, B.: A novel physical based meta-heuristic optimization method known as lightning attachment procedure optimization. Appl. Soft Comput. J. 59, 596–621 (2017). https://doi.org/10.1016/j.asoc.2017.06.033 CrossRef

55.

Derringer, G.; Suich, R.: Simultaneous optimization of several response variables. J. Qual. Technol. 12, 214–219 (1980). https://doi.org/10.1080/00224065.1980.11980968 CrossRef

56.

Díaz-Cortés, M.A.; Cuevas, E.; Gálvez, J.; Camarena, O.: A new metaheuristic optimization methodology based on fuzzy logic. Appl. Soft Comput. J. 61, 549–569 (2017). https://doi.org/10.1016/j.asoc.2017.08.038 CrossRef

57.

Zhou, J.; Li, C.; Arslan, C.A.; Hasanipanah, M.; Bakhshandeh Amnieh, H.: Performance evaluation of hybrid FFA-ANFIS and GA-ANFIS models to predict particle size distribution of a muck-pile after blasting. Eng. Comput. (2019). https://doi.org/10.1007/s00366-019-00822-0 CrossRef

58.

Foroughi Nematollahi, A.; Rahiminejad, A.; Vahidi, B.: A novel multi-objective optimization algorithm based on lightning attachment procedure optimization algorithm. Appl. Soft Comput. J. 75, 404–427 (2019). https://doi.org/10.1016/j.asoc.2018.11.032 CrossRef

59.

Zheng, T.; Luo, W.: An enhanced lightning attachment procedure optimization with quasi-opposition-based learning and dimensional search strategies. Comput. Intell. Neurosci. 2019, 1–24 (2019). https://doi.org/10.1155/2019/1589303 CrossRef

60.

Chen, G.; Gou, Y.; Zhang, A.: Synthesis of compliant multistable mechanisms through use of a single bistable mechanism. J. Mech. Des. Trans. ASME (2011). https://doi.org/10.1115/1.4004543 CrossRef

61.

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. (2016). https://doi.org/10.1007/s12541-016-0126-5 CrossRef

62.

Dao, T.-P.; Huang, S.-C.: Design, fabrication, and predictive model of a 1-dof translational flexible bearing for high precision mechanism. Trans. Can. Soc. Mech. Eng. 39, 419–429 (2015)CrossRef

63.

García, S.; Fernández, A.; Luengo, J.; Herrera, F.: A study of statistical techniques and performance measures for genetics-based machine learning: accuracy and interpretability. Soft. Comput. (2009). https://doi.org/10.1007/s00500-008-0392-y CrossRef

64.

Li, L.M.; Lu, K.Di; Zeng, G.Q.; Wu, L.; Chen, M.R.: A novel real-coded population-based extremal optimization algorithm with polynomial mutation: a non-parametric statistical study on continuous optimization problems. Neurocomputing (2016). https://doi.org/10.1016/j.neucom.2015.09.075 CrossRef

65.

García, S.; Molina, D.; Lozano, M.; Herrera, F.: A study on the use of non-parametric tests for analyzing the evolutionary algorithms’ behaviour: a case study on the CEC’2005 special session on real parameter optimization. J. Heuristics 15, 617–644 (2009). https://doi.org/10.1007/s10732-008-9080-4 CrossRefMATH

Titel: A Multi-response Optimal Design of Bridge Amplification Mechanism Based on Efficient Approach of Desirability, Fuzzy Logic, ANFIS and LAPO Algorithm
verfasst von: Ngoc Le Chau
Ngoc Thoai Tran
Thanh-Phong Dao
Publikationsdatum: 06.05.2020
Verlag: Springer Berlin Heidelberg
Erschienen in: Arabian Journal for Science and Engineering / Ausgabe 7/2020
Print ISSN: 2193-567X
Elektronische ISSN: 2191-4281
DOI: https://doi.org/10.1007/s13369-020-04587-3

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