Title :
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
fDate :
Sept. 30 2008-Oct. 3 2008
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;
Conference_Titel :
Electronics, Robotics and Automotive Mechanics Conference, 2008. CERMA '08
Conference_Location :
Morelos
Print_ISBN :
978-0-7695-3320-9
DOI :
10.1109/CERMA.2008.70