Title: Recurrent neural networks to model input-output relationships of metal inert gas (MIG) welding process

Authors: Geet Lahoti; Dilip Kumar Pratihar

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

Abstract: The mechanical strength of weld-bead is dependent on its geometric parameters like bead height, width and penetration, which depend on input process parameters, namely welding speed, arc voltage, wire feed rate, gas flow rate, nozzle-to-plate distance, torch angle etc. Recurrent neural networks were used for conducting both forward and reverse mappings using three approaches. The first approach dealt with the training of Elman network through updating its connecting weights using a back-propagation algorithm. In second approach, a real-coded genetic algorithm was used along with the back-propagation algorithm to tune the network. The third approach utilised a real-coded genetic algorithm only to optimise the network. In forward mapping, third approach was found to outperform the others, but in reverse mapping, first and second approaches were seen to perform better than the third one. The performances of these approaches were found to be data dependent.

Keywords: recurrent neural networks; RNNs; genetic algorithms; MIG welding process; forward mapping; reverse mapping.

DOI: 10.1504/IJDATS.2017.086629

International Journal of Data Analysis Techniques and Strategies, 2017 Vol.9 No.3, pp.248 - 282

Received: 22 Apr 2015
Accepted: 22 Feb 2016
Published online: 15 Sep 2017 *

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