Optimization in the Resistant Spot-Welding Process of AZ61 Magnesium Alloy

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AFSHARI, Davood ;GHAFFARI, Ali ;BARSOUM, Zuheir .
Optimization in the Resistant Spot-Welding Process of AZ61 Magnesium Alloy. 
Strojniški vestnik - Journal of Mechanical Engineering, [S.l.], v. 68, n.7-8, p. 485-492, july 2022. 
ISSN 0039-2480.
Available at: <https://www.sv-jme.eu/article/optimization-in-resistant-spot-welding-process-of-az61-magnesium-alloy/>. Date accessed: 17 jun. 2024. 
doi:http://dx.doi.org/10.5545/sv-jme.2022.174.
Afshari, D., Ghaffari, A., & Barsoum, Z.
(2022).
Optimization in the Resistant Spot-Welding Process of AZ61 Magnesium Alloy.
Strojniški vestnik - Journal of Mechanical Engineering, 68(7-8), 485-492.
doi:http://dx.doi.org/10.5545/sv-jme.2022.174
@article{sv-jmesv-jme.2022.174,
	author = {Davood  Afshari and Ali  Ghaffari and Zuheir  Barsoum},
	title = {Optimization in the Resistant Spot-Welding Process of AZ61 Magnesium Alloy},
	journal = {Strojniški vestnik - Journal of Mechanical Engineering},
	volume = {68},
	number = {7-8},
	year = {2022},
	keywords = {resistance spot welding; residual stresses; artificial neural network; genetic algorithm; AZ61 magnesium alloy; },
	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.},
	issn = {0039-2480},	pages = {485-492},	doi = {10.5545/sv-jme.2022.174},
	url = {https://www.sv-jme.eu/article/optimization-in-resistant-spot-welding-process-of-az61-magnesium-alloy/}
}
Afshari, D.,Ghaffari, A.,Barsoum, Z.
2022 July 68. Optimization in the Resistant Spot-Welding Process of AZ61 Magnesium Alloy. Strojniški vestnik - Journal of Mechanical Engineering. [Online] 68:7-8
%A Afshari, Davood 
%A Ghaffari, Ali 
%A Barsoum, Zuheir 
%D 2022
%T Optimization in the Resistant Spot-Welding Process of AZ61 Magnesium Alloy
%B 2022
%9 resistance spot welding; residual stresses; artificial neural network; genetic algorithm; AZ61 magnesium alloy; 
%! Optimization in the Resistant Spot-Welding Process of AZ61 Magnesium Alloy
%K resistance spot welding; residual stresses; artificial neural network; genetic algorithm; AZ61 magnesium alloy; 
%X 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.
%U https://www.sv-jme.eu/article/optimization-in-resistant-spot-welding-process-of-az61-magnesium-alloy/
%0 Journal Article
%R 10.5545/sv-jme.2022.174
%& 485
%P 8
%J Strojniški vestnik - Journal of Mechanical Engineering
%V 68
%N 7-8
%@ 0039-2480
%8 2022-07-05
%7 2022-07-05
Afshari, Davood, Ali  Ghaffari, & Zuheir  Barsoum.
"Optimization in the Resistant Spot-Welding Process of AZ61 Magnesium Alloy." Strojniški vestnik - Journal of Mechanical Engineering [Online], 68.7-8 (2022): 485-492. Web.  17 Jun. 2024
TY  - JOUR
AU  - Afshari, Davood 
AU  - Ghaffari, Ali 
AU  - Barsoum, Zuheir 
PY  - 2022
TI  - Optimization in the Resistant Spot-Welding Process of AZ61 Magnesium Alloy
JF  - Strojniški vestnik - Journal of Mechanical Engineering
DO  - 10.5545/sv-jme.2022.174
KW  - resistance spot welding; residual stresses; artificial neural network; genetic algorithm; AZ61 magnesium alloy; 
N2  - 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.
UR  - https://www.sv-jme.eu/article/optimization-in-resistant-spot-welding-process-of-az61-magnesium-alloy/
@article{{sv-jme}{sv-jme.2022.174},
	author = {Afshari, D., Ghaffari, A., Barsoum, Z.},
	title = {Optimization in the Resistant Spot-Welding Process of AZ61 Magnesium Alloy},
	journal = {Strojniški vestnik - Journal of Mechanical Engineering},
	volume = {68},
	number = {7-8},
	year = {2022},
	doi = {10.5545/sv-jme.2022.174},
	url = {https://www.sv-jme.eu/article/optimization-in-resistant-spot-welding-process-of-az61-magnesium-alloy/}
}
TY  - JOUR
AU  - Afshari, Davood 
AU  - Ghaffari, Ali 
AU  - Barsoum, Zuheir 
PY  - 2022/07/05
TI  - Optimization in the Resistant Spot-Welding Process of AZ61 Magnesium Alloy
JF  - Strojniški vestnik - Journal of Mechanical Engineering; Vol 68, No 7-8 (2022): Strojniški vestnik - Journal of Mechanical Engineering
DO  - 10.5545/sv-jme.2022.174
KW  - resistance spot welding, residual stresses, artificial neural network, genetic algorithm, AZ61 magnesium alloy, 
N2  - 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.
UR  - https://www.sv-jme.eu/article/optimization-in-resistant-spot-welding-process-of-az61-magnesium-alloy/
Afshari, Davood, Ghaffari, Ali, AND Barsoum, Zuheir.
"Optimization in the Resistant Spot-Welding Process of AZ61 Magnesium Alloy" Strojniški vestnik - Journal of Mechanical Engineering [Online], Volume 68 Number 7-8 (05 July 2022)

Authors

Affiliations

  • University of Zanjan, Iran 1
  • Royal Institute of Technology, Sweden 2

Paper's information

Strojniški vestnik - Journal of Mechanical Engineering 68(2022)7-8, 485-492
© The Authors 2022. CC BY 4.0 Int.

https://doi.org/10.5545/sv-jme.2022.174

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.

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