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Multi-objective robust design optimization of a sewing mechanism under uncertainties

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Abstract

This work deals with the multi-objective robust design optimization of a needle-bar-and-thread-take-up-lever (NBTTL) mechanism used in sewing machines. A combined multi-objective imperialist competitive algorithm and Monte Carlo method are developed and used for the robust multi-objective optimization of the NBTTL mechanism. This robust optimization considers simultaneously the Needle Jerk, the transmission angle, the coupler tracking error and their standard deviations where the design parameters uncertainties are considered. The obtained results showed that the robust design reduces significantly the sensitivity of the NBTTL performances to the design parameters uncertainties compared to the deterministic one and to the commercialized Juki 8700 machine.

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Abbreviations

NJ:

Needle Jerk index

TE:

Tracking error of the coupler point

TA:

Transmission angle index

DP:

Design parameters

D(DP):

The search domain of DP

SR:

Robust solution

SD:

Deterministic solution

\({\upgamma }\) :

Deviation from original direction of colony

\(\hbox {Cost}_{\mathrm{imp}}\) :

Cost of an imperialist

\(\hbox {Cost}_{\mathrm{col}}\) :

Cost of a colony

\(\hbox {N}_{\mathrm{col}}\) :

Number of colonies

\(\hbox {C}_\mathrm{n} \) :

The normalized cost of the \(\hbox {n}{\mathrm{th}}\) imperialist

\(\hbox {P}_\mathrm{n} \) :

The normalized power of \(\hbox {n}{\mathrm{th}}\) imperialist

\(\hbox {q}_\mathrm{i} \) :

\(\hbox {i}{\mathrm{th}}\) NBTTL link measurement value

\(\bar{{\upsigma }}\) :

Standard deviation of \(\bar{\hbox {q}}\)

\(\bar{\hbox {q}}\) :

Mean value of a series \(\hbox {q}_\mathrm{i} \)

\(\upbeta \) :

Assimilation coefficient

\({\uptheta }\) :

Parameter of colonies’ deviation

N:

Number of empires

\({\upmu }\) :

Transmission angle of motion

\(\hbox {X}\) :

Direction parameter of colonies motion

\(\hbox {d}\) :

Distance between a colony and an imperialist

\(\hbox {f}_{\mathrm{j,n}} \) :

The value of the objective function j for the imperialist n

\(\hbox {f}_\mathrm{j}^{\min } \) :

The minimum values of objective function j in each iteration

\(\hbox {P}_{\mathrm{p}_\mathrm{n} } \) :

The possession probability of the \(\hbox {n}{\mathrm{th}}\) empire

\(\hbox {NTC}_\mathrm{n} \) :

The normalized total cost of the \(\hbox {n}{\mathrm{th}}\) empire

References

  • Al-Aomar, R. (2006). Incorporating robustness into genetic algorithm search of stochastic simulation outputs. Simulation Modelling Practice and Theory, 14, 201–223.

    Article  Google Scholar 

  • Araujo, A., Silvano, S., & Martins, N. (2014). Monte Carlo uncertainty simulation of surface emissivity at ambient temperature obtained by dual spectral infrared radiometry. Infrared Physics and Technology, 67, 131–137.

    Article  Google Scholar 

  • Atashpaz-Gargari, E., & Lucas, C. (2007). Imperialist competitive algorithm: An algorithm for optimization inspired by imperialistic competition. In IEEE congress on evolutionary computation (pp. 4661–4667).

  • Berkani, S., Yallese, M., Boulanouar, L., & Mabrouki, T. (2015). Statistical analysis of AISI304 austenitic stainless steel machining using Ti(C, N)/Al2O3/TiN CVD coated carbide tool. International Journal of Industrial Engineering Computations, 6(4), 539–552.

    Article  Google Scholar 

  • Bouazizi, M. L., Ghanmi, S., Nasri, R., & Bouhaddi, N. (2009). Robust optimization of the non-linear behavior of a vibrating system. European Journal of Mechanics A: Solids, 28, 141–154.

    Article  Google Scholar 

  • Chandrashekhar, M., & Ganguli, R. (2009). Uncertainty handling in structural damage detection using fuzzy Logic and probabilistic simulation. Mechanical Systems and Signal Processing, 23, 384–404.

    Article  Google Scholar 

  • Cheng, S., & Li, M. (2015). Robust optimization using hybrid differential evolution and sequential quadratic programming. Engineering Optimization, 47(1), 87–106.

    Article  Google Scholar 

  • Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002). A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Transaction on Evolutionary Computation, 6(2), 182–197.

    Article  Google Scholar 

  • Ebrahimi, S., Hajizadeh, I., & Payvandy, P. (2014). Multi-objective constrained optimization of a newly developed needle driving mechanism in sewing machine for performance improvement. International Journal of Advanced Design and Manufacturing Technology, 7(4), 9–18.

    Google Scholar 

  • Ghanmi, S., Guedri, M., Bouazizi, M.-L., & Bouhaddi, N. (2011). Robust multi-objective and multi-level optimization of complex mechanical structures. Mechanical Systems and Signal Processing, 25(7), 2444–2461.

    Article  Google Scholar 

  • Gotlih, K., Lojen, D. Z., & Vohar, B. (2007). Optimization of needle penetration velocity using the link drive mechanism in a sewing machine. Fibres and Textiles in Eastern Europe, 15, 66–71.

    Google Scholar 

  • Jamali, A., Khaleghi, E., Gholaminezhad, I., Nariman-Zadeh, N., Gholaminia, B., & Jamal-Omidi, A. (2014). Multi-objective genetic programming approach for robust modeling of complex manufacturing processes having probabilistic uncertainty in experimental data. Journal of Intelligent Manufacturing. doi:10.1007/s10845-014-0967-7.

  • Kalantari, M., Dong, C., & Davies, Ian J. (2016). Multi-objective robust optimization of unidirectional carbon/glass fibre reinforced hybrid composites under flexural loading. Composite Structures, 138, 264–275.

    Article  Google Scholar 

  • Kamerec, J., Beran, J., Lima, M., Machado, M., Machado, J. M., & Silva, J. P. (2012). Finding the optimal setting of the sewing needle transfer mechanism using simulation software. TRS Textile Research Symposium, Portugal, 41, 1–4.

    Google Scholar 

  • Matthew, B., James, H., & Gray, K. (2015). A unified approach to measurement error and missing data: Overview and application. Sociological Methods and Research. doi:10.1177/0049124115585360.

  • Mishra, P. C., Das, D. K., Ukamanal, M., Routara, B. C., & Sahoo, A. K. (2015). Multi-response optimization of process parameters using Taguchi method and grey relational analysis during turning AA 7075/SiC composite in dry and spray cooling environments. International Journal of Industrial Engineering Computations, 6, 445–456.

  • Mohammadi, M., Jolai, F., & Reza, T.-M. (2013). Solving a new stochastic multi-mode p-hube covering location problem considering risk by a novel multi-objective algorithm. Applied Mathematical Modelling, 37, 10053–10073.

    Article  Google Scholar 

  • Motevalli, M., Zadbar, A., Elyasi, E., & Jalaal, M. (2015). Using Monte-Carlo approach for analysis of quantitative and qualitative operation of reservoirs system with regard to the inflow uncertainty. Journal of African Earth Sciences, 105, 1–16.

    Article  Google Scholar 

  • Murugan, S., Ganguli, R., & Harursampath, D. (2008). Aero-elastic response of composite helicopter rotor with random material properties. Journal of Aircraft, 45(1), 306–322.

    Article  Google Scholar 

  • Najlawi, B., Nejlaoui, M., Affi, Z., & Romdhane, L. (2016). An improved imperialist competitive algorithm for multi-objective optimization. Engineering Optimization, 48(11), 1823–1844.

    Article  Google Scholar 

  • Nejlaoui, M., Houidi, A., Affi, Z., & Romdhane, L. (2013). Multi-objective robust design optimization of rail vehicle moving in short radius curved tracks based on the safety and comfort criteria. Simulation Modelling Practice and Theory, 30, 21–34.

    Article  Google Scholar 

  • Nekooghadirli, N., Tavakkoli-Moghaddam, R., Ghezavati, V. R., & Javanmard, S. (2014). Solving a new bi-objective location-routing-inventory problem in a distribution network by meta-heuristics. Computers and Industrial Engineering, 76, 204–221.

    Article  Google Scholar 

  • Payvandy, P., & Ebrahimi, S. (2015). Optimization of the thread take-up lever mechanism in lockstitch sewing machine using the imperialistic competitive algorithm. Journal of Textile and Polymers, 3, 12–18.

    Google Scholar 

  • Sargade, V. G., Nipanikar, S. R., & Meshram, S. M. (2016). Analysis of surface roughness and cutting force during turning of Ti6Al4V ELI in dry environment. International Journal of Industrial Engineering Computations, 7, 257–266.

    Article  Google Scholar 

  • Shrinivas, S. B., & Chand, S. (2002). Transmission angle in mechanisms. Mechanism and Machine Theory, 37, 175–195.

    Article  Google Scholar 

  • Tebassi, H., Yallese, M. A., Khettabi, R., Belhadi, S., Meddour, I., & Francois, G. (2016). Multi-objective optimization of surface roughness, cutting forces, productivity and power consumption when turning of Inconel 718. International Journal of Industrial Engineering Computations, 7, 111–134.

    Article  Google Scholar 

  • Vallerio, M., Dries, T., Lorenzo, C., Flavio, M., JanVan, I., & Filip, L. (2016). Robust multi-objective dynamic optimization of chemical processes using the Sigma Point method. Chemical Engineering Science, 140, 201–216.

    Article  Google Scholar 

  • Wenchao, Y., Yinzhi, Z., Liang, G., Xinyu, L., & Chunjiang, Z. (2016). Engineering design optimization using an improved local search based epsilon differential evolution algorithm. Journal of Intelligent Manufacturing,. doi:10.1007/s10845-016-1199-9.

    Article  Google Scholar 

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Correspondence to Bilel Najlawi.

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Najlawi, B., Nejlaoui, M., Affi, Z. et al. Multi-objective robust design optimization of a sewing mechanism under uncertainties. J Intell Manuf 30, 783–794 (2019). https://doi.org/10.1007/s10845-016-1284-0

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