Effects of Process Parameters on the Weld Quality During Double-Pulsed Gas Metal Arc Welding of 2205 Duplex Stainless Steel

2018 ◽  
pp. 113-129
Author(s):  
Yu Hu ◽  
Jiaxiang Xue
Keyword(s):  
Stainless Steel ◽  
Duplex Stainless Steel ◽  
Process Parameters ◽  
Arc Welding ◽  
Gas Metal Arc Welding ◽  
Weld Quality ◽  
2205 Duplex Stainless Steel ◽  
Metal Arc Welding

On the Effectiveness of Duplex Stainless Steel Cladding Deposited by Gas Metal Arc Welding

2014 ◽  
Vol 47 (4) ◽  
pp. 24 ◽  
Author(s):  
Anup Kumar Verma ◽  
Bidhan Chandra Biswas ◽  
Protap Roy ◽  
Samiran De ◽  
Sukanta Saren ◽  
...  
Keyword(s):  
Stainless Steel ◽  
Duplex Stainless Steel ◽  
Arc Welding ◽  
Gas Metal Arc Welding ◽  
Metal Arc Welding

Influence of multipass pulsed gas metal arc welding on corrosion behaviour of a duplex stainless steel

2014 ◽  
Vol 86 ◽  
pp. 268-274 ◽  
Author(s):  
Thiago Chehuan ◽  
Vanessa Dreilich ◽  
Kioshy S. de Assis ◽  
Flávio V.V. de Sousa ◽  
Oscar R. Mattos
Keyword(s):  
Stainless Steel ◽  
Duplex Stainless Steel ◽  
Arc Welding ◽  
Corrosion Behaviour ◽  
Gas Metal Arc Welding ◽  
Metal Arc Welding

scholarly journals Parameter Optimization of Hybrid-Tandem Gas Metal Arc Welding Using Analysis of Variance-Based Gaussian Process Regression

Metals ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 1087
Author(s):  
Jin Young Kim ◽  
Dae Young Lee ◽  
Jaeyoung Lee ◽  
Seung Hwan Lee
Keyword(s):  
Gaussian Process ◽  
Analysis Of Variance ◽  
Parameter Optimization ◽  
Process Parameters ◽  
Arc Welding ◽  
Gas Metal Arc Welding ◽  
Gaussian Process Regression ◽  
Weld Quality ◽  
Metal Arc Welding ◽  
Tandem Gmaw

In this paper, the parameter optimization of the hybrid-tandem gas metal arc welding (GMAW) process was studied. The hybrid-tandem GMAW process uses an additional filler-wire with opposite polarity in contrast to the conventional tandem process. In this process, more process parameters and the relationship between the parameters causing strong nonlinearity should be considered. The analysis of variance-based Gaussian process regression (ANOVA-GPR) method was implemented to construct surrogate modeling, which can express nonlinearity including uncertainty of weld quality. Major parameters among several process parameters in this welding process can be extracted by use of this novel method. The weld quality used as a cost function in the optimization of process parameters is defined by characteristics related to penetration and bead shape, and the sequential quadratic programming (SQP) method was used to determine the optimal welding condition. This approach enabled sound weld quality at a high travel speed of 1.9 m/min, which is difficult to achieve in the hybrid-tandem GMAW process.

Modeling and Optimization of Gas Metal Arc Welding (GMAW) Process

2012 ◽  
pp. 339-367
Author(s):  
R. Venkata Rao
Keyword(s):  
Process Parameters ◽  
Arc Welding ◽  
Gas Metal Arc Welding ◽  
Welding Process ◽  
Optimization Methods ◽  
Optimization Techniques ◽  
Modeling And Optimization ◽  
Metal Arc Welding ◽  
Process Output ◽  
Set Up

Weld quality is greatly affected by the operating process parameters in the gas metal arc welding (GMAW) process. The quality of the welded material can be evaluated by many characteristics, such as bead geometric parameters, deposition efficiency, weld strength, weld distortion, et cetera. These characteristics are controlled by a number of welding process parameters, and it is important to set up proper process parameters to attain good quality. Various optimization methods can be applied to define the desired process output parameters through developing mathematical models to specify the relationship between the input parameters and output parameters. The method capable of accurate prediction of welding process output parameters would be valuable for rapid development of welding procedures and for developing control algorithms in automated welding applications. This chapter presents the details of various techniques used for modeling and optimization of GMAW process parameters. The optimization methods covered in this chapter are appropriate for modeling and optimizing the GMAW process. It is found that there is high level of interest in the adaptation of RSM and ANN techniques to predict responses and to optimize the GMAW process. Combining two optimization techniques, such as GA and RSM, would reveal good results for finding out the optimal welding conditions. Furthermore, efforts are required to apply advanced optimization techniques to find out the optimal parameters for GMAW process at which the process could be considered safe and more economical.

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