Prediction of laser cutting heat affected zone by extreme learning machine

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@Article{Anicic:2017:OLE,

• author = "Obrad Anicic and Srdan Jovic and Hivzo Skrijelj and Bogdan Nedic",
• title = "Prediction of laser cutting heat affected zone by extreme learning machine",
• journal = "Optics and Lasers in Engineering",
• volume = "88",
• pages = "1--4",
• year = "2017",
• ISSN = "0143-8166",
• DOI = "doi:10.1016/j.optlaseng.2016.07.005",
• URL = "http://www.sciencedirect.com/science/article/pii/S0143816616301385",
• abstract = "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 used effectively in applications of HAZ forecasting.",
• keywords = "genetic algorithms, genetic programming, Extreme Learning Machine, Forecasting, HAZ, Laser cutting",

}

Genetic Programming entries for Obrad Anicic Srdan Jovic Hivzo Skrijelj Bogdan Nedic

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