To study the influence of process parameters on weld bead geometry in shielded metal arc welding

Pathak, Deepak; Singh, Rudra Pratap; Gaur, Sumit; Balu, Vincent

doi:10.1016/j.matpr.2020.06.164

Cited by 11 publications

(5 citation statements)

References 3 publications

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“…The bead geometry includes the width, height, and penetration depth, as shown in Figure 1. Many welding parameters affect the bead geometry, such as the shielding gas, welding current, voltage, and welding speed [21,22]. The convexity index (H/W) of a weld is significantly influenced by thermal input and feeding speed.…”

Section: Introductionmentioning

confidence: 99%

Impact of Gas Metal Arc Welding Parameters on Bead Geometry and Material Distortion of AISI 316L

Khrais,

Al Hmoud,

Abdel Al

et al. 2023

JMMP

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This study investigates the impact of gas metal arc welding (GMAW) parameters on the bead geometry and material distortion of AISI 316L. Three parameters—arc current in ampere (A), filler feed rate (m/min), and gas composition—were modified at varying levels in order to examine their effects. This study sheds new light on MAG welding lines’ physical properties and behavior and highlights the influence of quaternary shielding gas compositions. Taguchi analysis, which includes signal-to-noise (S/N) ratio and analysis of variance (ANOVA), was utilized to analyze and optimize the welding parameters. This study found that arc current significantly impacts bead geometry, while the shielding gas composition has the most significant effect on angular distortion and transverse shrinkage. The optimal welding parameters for achieving the best bead height and width are 160 A, 3.5 m/min, G1, with a bead height of 4.89 mm, and 120 A, 3 m/min, G2, with a bead width of 6.69 mm. Moreover, the optimal welding parameters for minimizing both angular distortion and transverse shrinkage are 120 A, 4 m/min, G2, resulting in an angular distortion value of 0.0042° and a transverse shrinkage value of 0.0254 mm. This research has practical implications for improving welding performance and can contribute to the advancement of MAG and MIG welding in manufacturing applications.

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Section: Introductionmentioning

confidence: 99%

Impact of Gas Metal Arc Welding Parameters on Bead Geometry and Material Distortion of AISI 316L

Khrais,

Al Hmoud,

Abdel Al

et al. 2023

JMMP

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“…Among all the existing techniques, the arc welding processes produce coalescence of metals by heating them with an arc between an electrode and the workpiece. The first applied arc welding process was the carbon arc welding (CAW) [3] followed soon by others, such as shielded metal arc welding (SMAW) [4], gas tungsten arc welding (GTAW) [5], gas metal arc welding (GMAW) [6], and submerged arc welding (SAW) [7]. They differ from each other for the typology of electrodes used to generate the arc [1].…”

Section: Introductionmentioning

confidence: 99%

Submerged arc welding process: a numerical investigation of temperatures, displacements, and residual stresses in ASTM A516-Gr70 corner joined samples

Conte

David

Gagliardi

2023

Int J Adv Manuf Technol

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Arc welding processes represent an important category of fusion welding techniques. They are characterised by the use of an electric arc to create heat to melt and join metals. Submerged arc welding belongs to this process category. Its peculiarities are mainly related to the employment of a continuously fed electrode and of a powdered flux to cover the arc providing electrical conduction between electrode and metals to be connected and, at the same time, generating protective gas and slag shields. Current ($$I$$ I ), voltage ($$V$$ V ), and welding speed ($$v$$ v ) are the main process variables, properly set, to guarantee the specific heat (SH) required to achieve sound weld beads. In this research, finite element models were built looking at the macrographs extracted by experimental tests performed in two steps in order to obtain the real area of each pass and setting different welding conditions. Different combinations of $$I$$ I , $$V$$ V , and $$v$$ v were proposed providing, however, a fixed SH to the welding zone according to the required industrial standards. Specifically, tests were executed increasing the ratio V to v (for a constant $$I$$ I ) and the ratio $$I$$ I to $$v$$ v (for a constant $$V$$ V ) and with a different combination of $$V$$ V and $$I$$ I maintaining constant their product (for a constant $$v$$ v ). The influence of the investigated variables’ combination on the weld pool in terms of depth and width was discussed. Finally, the validated numerical models were employed to highlight precisely residual stresses and displacements trend on cross-sectioned weld beads connecting the shown tendencies to the investigated process conditions.

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“…However, in practical engineering applications, due to the complexity of the ship structure and various processes and limited by the environmental conditions, it is difficult to directly observe the geometric characteristics of the weld pool in real time, such as the front weld width, reinforcement, weld depth, etc. Many engineering practices and welder experiences [14], [15] show that there is a correlation between the geometric characteristics of the molten pool and the welding process parameters. Through the adjustment of the process parameters, the geometric characteristics of the molten pool can be predicted and controlled.…”

Section: Introductionmentioning

confidence: 99%

Improved Particle Filter-Based Modeling With Robotic GMAW for Weld Forming Prediction

Jiao,

Gao,

Feng

et al. 2023

IEEE Access

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To solve the knowledge modelling problem of strong nonlinear characteristics such as multivariable, high coupling and stochastic interference in the GMAW process of hull structures, a clustering similarity particle filter (CSPF) method based on the consistency principle of the state trajectory is proposed to establish a knowledge model for welding dynamics. The modelling mechanism of the stochastic process is selected to construct a Hammerstein model with uncertain noise in the relationship between the welding parameters and geometric characteristics of the weld pool, and the Hammerstein model is identified using a step response test of constant specification and two parameter identification algorithms: the recursive least squares method and the final prediction error criterion. Thus, the state space equation of the accurate identification structure is provided for knowledge process modelling. Relying on the particle filter algorithm as the core of the modelling framework and theoretical analysis, SIS filtering and GPF prediction methods are adopted to obtain the combined trajectory formed by the current (original) and future multistage (modified) spatial state information, and the state similarity between the combined trajectory and the actual system trajectory is measured by the cluster analysis method. A new proposed distribution is generated under the guidance of similarity measurement to improve the particle degradation phenomenon, update the firstorder Markov process with new observation information to compensate and modify the importance weight calculation, replace the resampling strategy to eliminate the particle depletion problem, and then establish the welding knowledge model of state tracking and forming prediction. Through simulation and experimentation of hull structure GMAW, it is concluded that both the training effect of state tracking and the prediction accuracy of weld formation can meet the process requirements of ship welding by particle filter and its improved method. Meanwhile, integrated with the convergence theorem of the CSPF algorithm, compared with the standard particle filter and auxiliary particle filter, the training effect of state tracking is better in the application of the CSPF method in the process knowledge modelling of hull structure GMAW, and the prediction results of weld forming have the advantages of higher accuracy, stronger robustness, and better timeliness.

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To study the influence of process parameters on weld bead geometry in shielded metal arc welding

Cited by 11 publications

References 3 publications

Impact of Gas Metal Arc Welding Parameters on Bead Geometry and Material Distortion of AISI 316L

Impact of Gas Metal Arc Welding Parameters on Bead Geometry and Material Distortion of AISI 316L

Submerged arc welding process: a numerical investigation of temperatures, displacements, and residual stresses in ASTM A516-Gr70 corner joined samples

Improved Particle Filter-Based Modeling With Robotic GMAW for Weld Forming Prediction

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