Model-based parameter optimization for arc welding process simulation

Chen, Fiona F.; Xiang, Junting; Thomas, David G.; Murphy, Anthony B.

doi:10.1016/j.apm.2019.12.014

Cited by 25 publications

(10 citation statements)

References 23 publications

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“…There, the importance of welding parameters, including the welding current, wire feed rate, workpiece thickness, speed, and welding geometry, was emphasized. This is also confirmed by the novelty of this work [10]. Pulsed MIG welding operates at two phases of heat input into the material.…”

Section: Introductionsupporting

confidence: 78%

Optimization of Synchropulsed MIG Welding Process Parameters for Welding of AW 5083 Sheets

et al. 2022

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Metal inert gas (MIG) welding is one of the processes most commonly used for joining metals, especially for joining aluminum and its alloys. The application of a pulsed current in an electric arc allows better controllability of the molten droplets and the arc transition, which subsequently leads to welds with characteristic flaky joints of better quality. In this paper, the optimization of parameters for welding aluminum alloys using the synchropulse welding process is investigated. By observing the input variables that have the greatest influence on the change in appearance of the welding current characteristics (delta wire feed from 0.1 to 6.0 m/min, frequency F from 0.5 to 3 Hz, duty cycle from 10% to 90%), it is possible to perform an optimization to achieve the desired output values. The output variables of the experiments are defined as insufficient/excessive throat thickness (mm), depth of penetration (mm), and weld width (mm); and for the desired quality of the welded joint the most acceptable range of its values is selected, the numerical optimization implementation. The experiment has shown that the delta wire feed has the greatest effect on the observed properties, while the influence of frequency F and duty cycle is somewhat smaller, but the factors responsible for the observed output properties are still significant. From all this, it is possible to select specific values of these input variables to define the best possible observed properties and to determine the characteristics of the defined mathematical models.

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Section: Introductionsupporting

confidence: 78%

Optimization of Synchropulsed MIG Welding Process Parameters for Welding of AW 5083 Sheets

et al. 2022

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“…Therefore, modeling and prediction of the geometrical errors are very complicated issues. The models and principles developed for welding process control, such as [37] [38] [39] and [40] can hardly be utilized for MLMB depositions.…”

Section: Introductionmentioning

confidence: 99%

Interlayer closed-loop control of forming geometries for wire and arc additive manufacturing based on fuzzy-logic inference

Zhang

et al. 2021

Journal of Manufacturing Processes

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“…Sathish et al 20 recently used RSM and Taguchi technique to optimize GTAW process. Chet et al 21 also studied this type of welding using a modelbased optimization for different alloys and then validated the results experimentally. Benyounis and Olabi 22 compared various techniques including GA, ANN, RSM, Taguchi, and Factorial Design for optimization of welding considering their response speed and accuracy.…”

Section: Introductionmentioning

confidence: 99%

Finite element analysis, prediction, and optimization of residual stresses in multi-pass arc welding with experimental evaluation

Amirsalari

Golabi

2022

The Journal of Strain Analysis for Engineering Design

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Prediction and reduction of unwanted tensile Residual Stress of welded stainless-steel plates is presented in this paper. Validated finite element analysis and Artificial Neural Network (ANN) is employed to simulate and mathematically model the process, respectively. Taguchi design of experiments tool is utilized to generate input data for finite element analyses and also to choose the most accurate ANN structures. RSs are minimized using three methods: Taguchi suggestion, Comprehensive factorial search, and Particle Swarm Optimization, whose accuracy and response pace increases and decreases respectively in this order. Furthermore, adding and removing extra weld lines was proposed to reduce unwanted residual stresses by up to 50%. Finally, the shapes and amounts of results are experimentally verified using contour method and proposed novel application of roughness testing. Micro-grain structures of the welded samples were also investigated, and RSs were discussed considering metallography images.

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Model-based parameter optimization for arc welding process simulation

Cited by 25 publications

References 23 publications

Optimization of Synchropulsed MIG Welding Process Parameters for Welding of AW 5083 Sheets

Optimization of Synchropulsed MIG Welding Process Parameters for Welding of AW 5083 Sheets

Interlayer closed-loop control of forming geometries for wire and arc additive manufacturing based on fuzzy-logic inference

Finite element analysis, prediction, and optimization of residual stresses in multi-pass arc welding with experimental evaluation

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