An artificial neural network approach for analysis and minimization of HAZ in CO2 laser cutting of stainless steel

被引：0
|
作者：
Madić, Miloš [1 ]
Brabie, Gheorghe [2 ]
Radovanović, Miroslav [1 ]
机构：
[1] Faculty of Mechanical Engineering, University of Niš, Serbia
[2] Faculty of Engineering, University of Bacau, Romania
来源：
UPB Scientific Bulletin, Series D: Mechanical Engineering | 2013年 / 75卷 / 02期
关键词：
Carbon dioxide - Heat affected zone - Neural networks - Laser beam cutting - Stainless steel - Carbon dioxide lasers - Laser beams;
D O I：
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中图分类号：
学科分类号：
摘要：
This paper present an approach for modeling and analysis of the effects of the laser cutting parameters on the width of HAZ obtained in CO2 laser cutting of stainless steel by using artificial neural network (ANN). ANN model was developed in terms of the specific laser energy (laser power to cutting speed ratio), assist gas pressure and focus position. Using the experimental data the ANN was trained with gradient descent with momentum algorithm and the average absolute percentage errors on training and testing were 3.68 % and 3.52%, respectively. In addition to modeling and analysis, through ANN simulation optimal cutting conditions with minimal width of HAZ were identified.
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页码：85 / 96
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