A hybrid computing scheme for forward and reverse mappings of metal inert gas welding process. (27th August 2010)

• Record Type: Journal Article
• Authors: Datta, Somak; Pratihar, Dilip Kumar
• Is Part Of: International journal of computational intelligence studies. Volume 1:Number 3(2010)
• Page Start: 256
• Page End: 272
• Publication Date: 2010-08-27
• Subjects: hybrid computing -- forward mapping -- reverse mapping -- metal inert gas welding -- fuzzy clustering -- radial basis function -- RBF neural network -- genetic algorithms -- back-propagation algorithms -- MIG welding; Computational intelligence -- Periodicals; 006.305
• Languages: English
• ISSNs: 1755-4985

Abstract

A hybrid computing scheme has been developed for carrying out forward and reverse mappings of metal inert gas welding process. As the input-output relationships of this process may not be the same over the entire range of the variables, the mappings have been done using the clusters made by the data points. Optimisation has been carried out to improve the performances of both fuzzy clustering techniques as well as radial basis function neural network used for the clustering and reasoning, respectively. Two approaches of hybrid computing scheme have been proposed, in which a binary-coded genetic algorithm has been utilised to decide the optimal structure of the network (through clustering of the data using two different approaches separately) and a back-propagation algorithm has been employed to determine the optimised parameters of the network. The hybrid scheme has yielded better performance compared to that developed using the genetic algorithm only.