Optimization in the resistant spot-welding process of AZ61 magnesium alloy

Davood Afshari (Author), Ali Ghaffari (Author), Zuheir Barsum (Author)

Abstract

In this paper, an integrated artificial neural network (ANN) and multi-objective genetic algorithm (GA) are developed to optimize the resistance spot welding (RSW) of AZ61 magnesium alloy. Since the stability and strength of a welded joint are strongly dependent on the size of the nugget and the residual stresses created during the welding process, the main purpose of the optimization is to achieve the maximum size of the nugget and minimum tensile residual stress in the weld zone. It is identified that the electrical current, welding time, and electrode force are the main welding parameters affecting the weld quality. The experiments are carried out based on the full factorial design of experiments (DOE). In order to measure the residual stresses, an X-ray diffraction technique is used. Moreover, two separate ANNs are developed to predict the nugget size and the maximum tensile residual stress based on the welding parameters. The ANN is integrated with a multi-objective GA to find the optimum welding parameters. The findings show that the integrated optimization method presented in this study is effective and feasible for optimizing the RSW joints and process.

Keywords

resistance spot welding;residual stresses;artificial neural network;genetic algorithm;AZ61 magnesium alloy;

Data

Language:	English
Year of publishing:	2022
Typology:	1.01 - Original Scientific Article
Organization:	UL FS - Faculty of Mechanical Engineering
UDC:	621.791
COBISS:	121920259
ISSN:	0039-2480
Views:	7
Downloads:	0
Average score:	0 (0 votes)
Metadata:

Other data

Secondary language:	Slovenian
Secondary title:	Optimizacija postopka uporovnega točkovnega varjenja magnezijeve zlitine AZ61
Secondary keywords:	uporovno točkovno varjenje;preostale napetosti;umetna nevronska mreža;genetski algoritem;magnezijeva zlitina AZ61;
Type (COBISS):	Article
Pages:	str. 485-492
Volume:	ǂVol. ǂ68
Issue:	ǂno. ǂ7/8
Chronology:	Jul./Aug. 2022
DOI:	10.5545/sv-jme.2022.174
ID:	16506315