Optimization of friction welding parameters using evolutionary computational techniques

The purpose of this study is to propose a method to decide near optimal settings of the welding process parameters in friction welding of stainless steel (AISI 304) by using non conventional techniques and artificial neural network (ANN). The methods suggested in this study were used to determine th...

Full description

Saved in:
Bibliographic Details
Published in:Journal of materials processing technology 2009-03, Vol.209 (5), p.2576-2584
Main Authors: Sathiya, P., Aravindan, S., Haq, A. Noorul, Paneerselvam, K.
Format: Article
Language:English
Subjects:
Artificial neural network (ANN)
Genetic algorithm (GA)
Metal loss
Particle swarm optimization (PSO)
Simulated annealing (SA)
Tensile strength
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The purpose of this study is to propose a method to decide near optimal settings of the welding process parameters in friction welding of stainless steel (AISI 304) by using non conventional techniques and artificial neural network (ANN). The methods suggested in this study were used to determine the welding process parameters by which the desired tensile strength and minimized metal loss were obtained in friction welding. This study describes how to obtain near optimal welding conditions over a wide search space by conducting relatively a smaller number of experiments. The optimized values obtained through these evolutionary computational techniques were compared with experimental results. The strength and microstructural aspects of the processed joints were also analyzed to validate the optimization.
ISSN:0924-0136
DOI:10.1016/j.jmatprotec.2008.06.030