Optimization Welding Process Parameters through Response Surface, Neural Network and Genetic Algorithms

Author

Praga-Alejo, R.J. ; Torres-Trevio, L.M. ; Pia-Monarrez, M.R.

Author_Institution

COMIMSA, Saltillo

fYear

2008

fDate

Sept. 30 2008-Oct. 3 2008

Firstpage

393

Lastpage

399

Abstract

Since the Neural Network (NN) with a Genetic Algorithm (GA) as a complement; are good optimization tools, we compare its performance with the Response Surface Methodology (RSM) that is generally used in the optimization of the process, in this case welding process. For the data used in the comparison, the results show that NN plus GA and RSM have a good results and very well performance, for identify the optimal set of parameters to obtain amaximum response of the process.

Keywords

genetic algorithms; neural nets; production engineering computing; response surface methodology; welding; genetic algorithms; neural network; response surface methodology; welding process parameters optimization; Artificial intelligence; Artificial neural networks; Automotive engineering; Eigenvalues and eigenfunctions; Genetic algorithms; Neural networks; Optimization methods; Response surface methodology; Robots; Welding; Artificial Intelligence; Genetic algorithms; Global Optimization; Neural Networks; Regression;

fLanguage

English

Publisher

ieee

Conference_Titel

Electronics, Robotics and Automotive Mechanics Conference, 2008. CERMA '08

Conference_Location

Morelos

Print_ISBN

978-0-7695-3320-9

Type

conf

DOI

10.1109/CERMA.2008.70

Filename

4641103