Recurrent neural networks to model input-output relationships of metal inert gas (MIG) welding process Online publication date: Fri, 15-Sep-2017
by Geet Lahoti; Dilip Kumar Pratihar
International Journal of Data Analysis Techniques and Strategies (IJDATS), Vol. 9, No. 3, 2017
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.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Data Analysis Techniques and Strategies (IJDATS):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com