Multiobjective optimization of multipass turning machining process using the Genetic Algorithms solution

• Autorzy: Amiolemhen Patrick Ejebheare , Eseigbe Joshua Ahurome

Identyfikatory

DOI

10.30464/jmee.2019.3.2.97

• Języki publikacji: EN

Abstrakty

EN

The study involves the development of multi-objective optimization model for turning machining process. This model was developed using a GA - based weighted-sum of minimum production cost and time criteria of multipass turning machining process subject to relevant technological/practical constraints. The results of the single-objective machining process optimization models for the multipass turning machining process when compared with those of multi-objective machining process model yielded the minimum production cost and minimum production time as $5.775 and 8.320 min respectively (and the corresponding production time and production cost as 12.996 min and $6.992, respectively), while those of the multi-objective machining process optimization model were $5.841and 9.097 min. Thus, the multi-objective machining process optimization model performed better than each of the single-objective model for the two criteria of minimum production cost and minimum production time respectively. The results also show that minimum production time model performs better than the minimum production cost model. For the example considered, the multi-objective model gave a lower production time of 30.0% than the corresponding production time obtained from the minimum production cost model, while it gave a lower production cost of 16.46% than the corresponding cost obtained by the minimum production time model.

Słowa kluczowe

EN

turning process; genetic algorithms; minimum production cost; minimum production time; single-objective model; multi-objective model

PL

toczenie; proces toczenia; algorytmy genetyczne; minimalny koszt produkcji; minimalny czas produkcji; model wielokryterialny

• Wydawca: Wydawnictwo Uczelniane Politechniki Koszalińskiej
• Czasopismo: Journal of Mechanical and Energy Engineering
• Rocznik: 2019
• Tom: Vol. 3, No 2
• Strony: 97--108
• Opis fizyczny: Bibliogr. 19 poz., tab., wykr.

Twórcy

autor

Amiolemhen Patrick Ejebheare

• Department of Production, Faculty of Engineering, University of Benin, P.M.B 1154, Benin City, Nigeria

autor

Eseigbe Joshua Ahurome

• Department of Production, Faculty of Engineering, University of Benin, P.M.B 1154, Benin City, Nigeria

Bibliografia

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• 17. Amiolemhen P.E., Eseigbe J.A. 2019. Genetic algorithms solution to the single-objective machining process optimization time model. Journal of Mechanical and Energy Engineering, Vol. 3(43), No. 1, pp. 13-24.
• 18. Amiolemhen, P.E. and Ibhadode, A. O. A. (2019). Development of Genetic Algorithms based weighted method for multi-objective optimization. Journal of the Nigerian Association of Mathematical Physics. Vol. 35, pp. 149-154.
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Uwagi

PL

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).