Please use this identifier to cite or link to this item: https://scidar.kg.ac.rs/handle/123456789/11606
Title: Prediction of laser cutting heat affected zone by extreme learning machine
Authors: Anicic O.
Jović M.
Skrijelj H.
Nedic, Bogdan
Issue Date: 2017
Abstract: © 2016 Elsevier Ltd Heat affected zone (HAZ) of the laser cutting process may be developed based on combination of different factors. In this investigation the HAZ forecasting, based on the different laser cutting parameters, was analyzed. The main goal was to predict the HAZ according to three inputs. The purpose of this research was to develop and apply the Extreme Learning Machine (ELM) to predict the HAZ. The ELM results were compared with genetic programming (GP) and artificial neural network (ANN). The reliability of the computational models were accessed based on simulation results and by using several statistical indicators. Based upon simulation results, it was demonstrated that ELM can be utilized effectively in applications of HAZ forecasting.
URI: https://scidar.kg.ac.rs/handle/123456789/11606
Type: article
DOI: 10.1016/j.optlaseng.2016.07.005
ISSN: 0143-8166
SCOPUS: 2-s2.0-84990948120
Appears in Collections:Faculty of Engineering, Kragujevac

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