Original scientific paper

https://doi.org/10.17559/TV-20140211234150

Multi-objective optimization of cut quality characteristic in CO2 laser cutting stainless steel

Miloš Madić orcid.org/0000-0002-2310-2590 ; Faculty of Mechanical Engineering, University of Niš, Aleksandra Medvedeva 14, 18000 Niš, Serbia
Miroslav Radovanović ; Faculty of Mechanical Engineering, University of Niš, Aleksandra Medvedeva 14, 18000 Niš, Serbia
Bogdan Nedić orcid.org/0000-0002-4236-3833 ; Faculty of Engineering, University of Kragujevac, Sestre Janjić 6, 34000 Kragujevac, Serbia
Vlatko Marušić ; Mechanical Engineering Faculty in Slavonski Brod, J. J. Strossmayer University of Osijek, Trg Ivane Brlić Mažuranić 2, 35000 Slavonski Brod, Croatia

Abstract

In this paper, multi-objective optimization of the cut quality characteristics in CO2 laser cutting of AISI 304 stainless steel was discussed. Three mathematical models for the prediction of cut quality characteristics such as surface roughness, kerf width and heat affected zone were developed using the artificial neural networks (ANNs). The laser cutting experiment was planned and conducted according to the Taguchi’s L27 orthogonal array and the experimental data were used to train single hidden layer ANNs using the Levenberg-Marquardt algorithm. The ANN mathematical models were developed considering laser power, cutting speed, assist gas pressure, and focus position as the input parameters. Multi-objective optimization problem was formulated using the weighting sum method in which the weighting factors that are used to combine cut quality characteristics into the single objective function were determined using the analytic hierarchy process method.

Keywords

analytic hierarchy process; artificial neural networks; CO2 laser cutting; cut quality characteristics; genetic algorithm; multi-objective optimization

Hrčak ID:

143116

URI

https://hrcak.srce.hr/143116

Publication date:

8.8.2015.

Article data in other languages: croatian

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