Multi-objective robust design optimization of a mechatronic system with uncertain parameters,using a polynomial chaos expansion method

Abstract

This article deals with the multi-objective robust design optimization of a mechatronic system used in sewing machines under uncertain design parameters. This mechatronic system is a needle bar and thread take-up lever mechanism driven by a direct current motor. The motor current and current fluctuation are minimized at the same time. The obtained results from a deterministic optimization showed a high sensitivity to design parameters. As an alternative, a robust multi-objective design optimization of the mechatronic system is conducted. For this goal, a multi-objective imperialist competitive algorithm combined with a polynomial chaos expansion method is investigated. Throughout a comparison with the deterministic results, we show that the robust performances of the mechatronic system are not sensible to design parameters’ uncertainties.

Keywords

Robust design optimization mechatronic sewing machine uncertain parameters polynomial chaos expansion current fluctuation

Get full access to this article

View all access options for this article.

References

Ebrahimi

Hajizadeh

Payvandy

. Multi-objective constrained optimization of a newly developed needle driving mechanism in sewing machine for performance improvement. Int J Adv Des Manuf Technol 2014; 7(4): 9–18.

Payvandy

Ebrahimi

. Optimization of the thread take-up lever mechanism in lockstitch sewing machine using the imperialistic competitive algorithm. J Textile Polymer 2015; 3: 12–18.

Najlawi

Nejlaoui

Affi

et al . Mechatronic design optimization of the mechanism in a sewing machine. Proc IMechE, Part C: J Mechanical Engineering Science. Epub ahead of print 6 January 2017. DOI: 10.1177/0954406216687786

Najlawi

Nejlaoui

Affi

et al . Multi-objective robust design optimization of a sewing mechanism under uncertainties. J Intell Manuf. Epub ahead of print 25 November 2016. DOI: 10.1007/s10845-016-1284-0

Deb

Pratap

Agarwal

et al . A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE T Evolut Comput 2002; 6(2): 182–197.

Coello

Maximino

. MOPSO: a proposal for multiple objective particle swarm optimization. In: Proceedings of the congress on evolutionary computation (CEC’2002), Piscataway, NJ, 12–17 May 2002, vol. 2, pp.1051–1056. New York: IEEE.

Pham

Castellani

. A modified bees algorithm and a statistics-based method for tuning its parameters. Proc IMechE, Part I: J Systems and Control Engineering 2012; 226(3): 287–301.

Najlawi

Nejlaoui

Affi

et al . An improved imperialist competitive algorithm for multi-objective optimization. Eng Optimiz 2016; 48(11): 1823–1844.

Mahdiyar

Hasanipanah

Armaghani

et al . A Monte Carlo technique in safety assessment of slope under seismic condition. Eng Comput. Epub ahead of print 19 January 2017. DOI: 10.1007/s00366-016-0499-1.

10.

Gholaminezhad

Assimi

Jamali

et al . Uncertainty quantification and robust modeling of selective laser melting process using stochastic multi-objective approach. Int J Adv Manuf Technol 2016; 86(5): 1425–1441.

11.

Poles

Lovison

. A polynomial chaos approach to robust multiobjective optimization. In: Hybrid and robust approaches to multiobjective optimization. Dagstuhl seminar proceedings (ed Deb

Greco

K Miettinen

et al .), Dagstuhl, 18–23 January 2009, No. 09041. Dagstuhl: Schloss Dagstuhl—Leibniz-Zentrum fuer Informatik.

12.

Rajabi

Behzad

Craig

. Polynomial chaos expansions for uncertainty propagation and moment independent sensitivity analysis of seawater intrusion simulations. J Hydrol 2015; 520: 101–122.

13.

Sandu

Blanchard

. Polynomial chaos-based parameter estimation methods applied to a vehicle system. Proc IMechE, Part K: J Multi-body Dynamics 2010; 224: 59–81.

14.

Streif

Kim

KKK

Rumschinski

et al . Robustness analysis, prediction and estimation for uncertain biochemical networks. IFAC Proc Vol 2013; 46(32): 1–20, https://dx-doi-org.web.bisu.edu.cn/10.3182/20131218-3-IN-2045.00190

15.

Konda

Singh

Singla

et al . Uncertainty propagation in puff-based dispersion models using polynomial chaos. Environ Modell Softw 2010; 25: 1608–1618.

16.

Nejlaoui

Houidi

Affi

et al . Multiobjective robust design optimization of rail vehicle moving in short radius curved tracks based on the safety and comfort criteria. Simul Model Pract Th 2013; 30: 21–34.

17.

Liu

Peng

Qiu

et al . Simulation-based robust design of complex product considering uncertainties of metamodel, design variables, and noise parameters. Proc IMechE, Part C: J Mechanical Engineering Science. Epub ahead of print 14 June 2016. DOI: 10.1177/0954406216654938

18.

Zhou

Yan

Huang

. Multi-objective robust controller design for spacecraft hovering around elliptical orbital target. Proc IMechE, Part I: Journal of Systems and Control Engineering 2015; 229(4): 356–371.

19.

Gundogdu

Erenturk

. Fuzzy control of a dc motor driven four-bar mechanism. Mechatronics 2005; 15: 423–438.

20.

EL-Kribi

Houidi

Affi

et al . Application of multi-objective genetic algorithms to the mechatronic design of a four bar system with continuous and discrete variables. Mech Mach Theory 2013; 61: 68–83.

21.

Armaghani

Hasanipanah

Edy

. A combination of the ICA-ANN model to predict air-overpressure resulting from blasting. Eng Comput 2016; 32(1): 155–171.

22.

Ghasemi

Ghavidel

Rahmani

et al . A novel hybrid algorithm of imperialist competitive algorithm and teaching learning algorithm for optimal power flow problem with non-smooth cost functions. Eng Appl Artif Intel 2014; 29: 54–69.

23.

Marano

Sgobba

Greco

et al . Robust optimum design of tuned mass dampers devices in random vibrations mitigation. J Sound Vib 2008; 313: 472–492.