Statistical modeling and optimization of resistance spot welding process parameters using neural networks and multi-objective genetic algorithm

Hamed Pashazadeh (), Yousof Gheisari and Mohsen Hamedi
Additional contact information
Hamed Pashazadeh: Islamic Azad University
Yousof Gheisari: Islamic Azad University
Mohsen Hamedi: University of Tehran

Journal of Intelligent Manufacturing, 2016, vol. 27, issue 3, No 5, 549-559

Abstract: Abstract Resistance spot welding (RSW) is a highly used joining procedure in automotive industry. In RSW, after a number of welds the welding electrode starts to wear and its diameter changes. This causes the weld nugget diameter abnormal variations and consequently reduces the weld strength. Therefore the tip of the electrode should be dressed in RSW. Selecting the optimum time for the welding electrode tip dressing operations is very important. In this research three welding parameters including the welding time, the welding current, and the welding pressure were identified as the main effective parameters on the weld nugget dimensions including the weld nugget diameter and height using full factorial design of experiments. Then using hybrid combination of the artificial neural networks and multi-objective genetic algorithm, the optimized values of the aforementioned parameters were specified. Finally experiments were fulfilled to estimate the admissible number of the weld spots which should be done before the electrode tip dressing operation.

Keywords: Resistance spot welding; Electrode tip dressing; Design of experiments; Artificial neural networks; Multi-objective genetic algorithm (search for similar items in EconPapers)
Date: 2016
References: View complete reference list from CitEc
Citations: View citations in EconPapers (8)

Downloads: (external link)
http://link.springer.com/10.1007/s10845-014-0891-x Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:joinma:v:27:y:2016:i:3:d:10.1007_s10845-014-0891-x

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10845

DOI: 10.1007/s10845-014-0891-x

Access Statistics for this article

Journal of Intelligent Manufacturing is currently edited by Andrew Kusiak

More articles in Journal of Intelligent Manufacturing from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().