Abstract
The aim of this paper is to present the results of the first step of a defined methodology for the neural network tool development. That first step is to studying the variables that have influence on extrusion process, especially in those that affect billet temperature and extrusion speed. In order to determine those parameters, a preliminary analysis was conducted with experimental data from real industry. Then, a multiple regression analysis was carried out to define which parameters will be the inputs of the neural network prediction tool.
Publisher
Trans Tech Publications, Ltd.
Subject
Mechanical Engineering,Mechanics of Materials,General Materials Science
Reference9 articles.
1. M. Reddy, H. Bertolini and H. Biel. HyperXtrude/Process. Extrusion Process Optimization Software, Proceedings of the Eighth International Aluminum Extrusion Technology Seminar Vol. 1(2004) p.23.
2. Anonymous Fichas técnicas A-GS (Aluminium Pechiney, 1987).
3. P. Saha and ASM International. Aluminum extrusion technology (ASM International, 2000).
4. M.L. Garcia-Romeu and J. Ciurana. Springback and geometry prediction - neural network applied to air bending process. Lecture Notes in Computer Science, Vol. 4113(2006), pp.470-475.
5. Z. Lozina, I. Duplancic and B. Lela. Optimization of aluminium extrusion and die design using neural networks and genentic algorithms, Aluminium Two Thousand 5th world congress, (2003).
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