Please use this identifier to cite or link to this item: https://scidar.kg.ac.rs/handle/123456789/8575
Title: Modelling of Kerf width in plasma jet metal cutting process using ANN approach
Authors: Peko I.
Nedic B.
Dordević A.
Veza I.
Journal: Tehnicki Vjesnik
Issue Date: 1-Apr-2018
Abstract: © 2018, Strojarski Facultet. All rights reserved. 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.
URI: https://scidar.kg.ac.rs/handle/123456789/8575
Type: Article
DOI: 10.17559/TV-20161024093323
ISSN: 13303651
SCOPUS: 85045892944
Appears in Collections:University Library, Kragujevac
[ Google Scholar ]

Page views(s)

16

Files in This Item:
File Description SizeFormat 
10.17559-TV-20161024093323.pdf1.31 MBAdobe PDFThumbnail
View/Open


Items in SCIDAR are protected by copyright, with all rights reserved, unless otherwise indicated.