Title: Modelling of input-output relationships of metal inert gas welding process using soft computing-based approaches

Authors: Somak Datta; Deepanshu; Dilip Kumar Pratihar

Addresses: Department of Mechanical Engineering, Soft Computing Laboratory, Indian Institute of Technology Kharagpur, Kharagpur – 721302, West Bengal, India ' Department of Mechanical Engineering, Soft Computing Laboratory, Indian Institute of Technology Kharagpur, Kharagpur – 721302, West Bengal, India ' Department of Mechanical Engineering, Soft Computing Laboratory, Indian Institute of Technology Kharagpur, Kharagpur – 721302, West Bengal, India

Abstract: Input-output relationships of metal inert gas welding process were determined in both forward and reverse directions, which are required in order to automate the same. Various types of neural networks, namely multi-layer feed-forward network, counter-propagation network and radial basis function network had been used for the said purpose. The networks were trained using back-propagation algorithm and/or genetic algorithm. Their performances were compared, and radial basis function network developed using the concept of clustering was found to perform better than other networks in terms of accuracy in prediction.

Keywords: metal inert gas welding; back-propagation neural network; counter-propagation neural network; CPNN; genetic algorithm; radial basis function neural network; RBFNN; fuzzy clustering.

DOI: 10.1504/IJCISTUDIES.2017.086044

International Journal of Computational Intelligence Studies, 2017 Vol.6 No.1, pp.1 - 28

Received: 10 Mar 2015
Accepted: 20 Jul 2015

Published online: 22 Aug 2017 *

Full-text access for editors Access for subscribers Purchase this article Comment on this article