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

Povzetek

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.

Ključne besede

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

Podatki

Jezik: Angleški jezik
Leto izida:
Tipologija: 1.01 - Izvirni znanstveni članek
Organizacija: UL FS - Fakulteta za strojništvo
UDK: 621.791
COBISS: 121920259 Povezava se bo odprla v novem oknu
ISSN: 0039-2480
Št. ogledov: 7
Št. prenosov: 0
Ocena: 0 (0 glasov)
Metapodatki: JSON JSON-RDF JSON-LD TURTLE N-TRIPLES XML RDFA MICRODATA DC-XML DC-RDF RDF

Ostali podatki

Sekundarni jezik: Slovenski jezik
Sekundarni naslov: Optimizacija postopka uporovnega točkovnega varjenja magnezijeve zlitine AZ61
Sekundarne ključne besede: uporovno točkovno varjenje;preostale napetosti;umetna nevronska mreža;genetski algoritem;magnezijeva zlitina AZ61;
Vrsta dela (COBISS): Članek v reviji
Strani: str. 485-492
Letnik: ǂVol. ǂ68
Zvezek: ǂno. ǂ7/8
Čas izdaje: Jul./Aug. 2022
DOI: 10.5545/sv-jme.2022.174
ID: 16506315
Priporočena dela:
, ni podatka o podnaslovu
, diplomsko delo univerzitetnega študijskega programa