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Optimization in the Resistant Spot-Welding Process of AZ61 Magnesium Alloy
Univ Zanjan, Zanjan, Iran..
Univ Zanjan, Zanjan, Iran..
KTH, School of Engineering Sciences (SCI), Engineering Mechanics, Vehicle Engineering and Solid Mechanics.ORCID iD: 0000-0003-4180-4710
2022 (English)In: Strojniski vestnik, ISSN 0039-2480, Vol. 68, no 7-8, p. 485-492Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
Faculty of Mechanical Engineering , 2022. Vol. 68, no 7-8, p. 485-492
Keywords [en]
resistance spot welding, residual stresses, artificial neural network, genetic algorithm, AZ61 magnesium alloy
National Category
Metallurgy and Metallic Materials
Identifiers
URN: urn:nbn:se:kth:diva-324794DOI: 10.5545/sv-jme.2022.174ISI: 000935371600004Scopus ID: 2-s2.0-85139764131OAI: oai:DiVA.org:kth-324794DiVA, id: diva2:1743795
Note

QC 20230316

Available from: 2023-03-16 Created: 2023-03-16 Last updated: 2023-03-16Bibliographically approved

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Barsoum, Zuheir

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CiteExportLink to record
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