Original scientific paper

https://doi.org/10.17559/TV-20161024093323

Modelling of Kerf Width in Plasma Jet Metal Cutting Process using ANN Approach

Ivan Peko orcid.org/0000-0001-5048-9842 ; Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture, University of Split, Ruđera Boškovića 32, 21000 Split, Croatia
Bogdan Nedić ; Faculty of Engineering, University of Kragujevac, Sestre Janjić 6, 34000 Kragujevac, Serbia
Aleksandar Đorđević orcid.org/0000-0003-2856-6578 ; Faculty of Engineering, University of Kragujevac, Sestre Janjić 6, 34000 Kragujevac, Serbia
Ivica Veža ; Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture, University of Split, Ruđera Boškovića 32, 21000 Split, Croatia

Abstract

In this paper Artificial Neural Network (ANN) model was developed for prediction of kerf width in plasma jet metal cutting process. Process parameters whose influence was analyzed are cutting height, cutting speed and arc current. An L18 (21x37) Taguchi orthogonal array experiment was conducted on aluminium sheet of 3 mm thickness. Using the experimental data a feed – forward backpropagation artificial neural network model was developed. After the prediction accuracy of the developed model was verified, the model was used to generate plots that show influence of process parameters and their interactions on analzyed kerf width and to get conlusions about process parameters values that lead to minimal kerf width.

Keywords

artificial neural networks; kerf width; modelling; plasma jet metal cutting

Hrčak ID:

199136

URI

https://hrcak.srce.hr/199136

Publication date:

21.4.2018.

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