Sensitivity analysis for process parameters in GMA welding processes using a factorial design method

Kim, I.S.; Son, Kwang-Jae; Yang, Young-Soo; Yaragada, P.K.D.V.

doi:10.1016/s0890-6955(03)00054-3

Cited by 148 publications

(97 citation statements)

References 2 publications

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“…The selection of the welding electrode wire based on matching the mechanical properties and physical characteristics of the base metal, weld size and existing electrode inventory (Kim et al, 2003). A candidate material for cladding which has excellent corrosion resistance and weld ability is stainless steel.…”

Section: Experimentationmentioning

confidence: 99%

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Prediction and optimization of weld bead geometry in gas metal arc welding process using RSM and fmincon

Sreeraj¹

2013

J. Mech. Eng. Res.

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Cladding is a surface modification process in which a specially designed alloy is surface welded in order to enhance corrosion resistant properties. Common cladding techniques include Gas Tungsten Arc Welding (GTAW), submerged arc welding (SAW) and gas metal arc welding (GMAW). Because of high reliability, easiness in operation, high penetration good surface finish and high productivity gas metal arc welding became a natural choice for fabrication industries. This paper presents central composite rotatable design with full replication techniques to predict four critical dimensions of bead geometry. The second order regression method was developed to study the correlations. The developed models have been checked for adequacy and significance. The main and interaction effects of process variables and bead geometry were presented in graphical form. Using fmincon function the process parameters were optimized.

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Section: Experimentationmentioning

confidence: 99%

“…Xmin is the lower limit of parameters and Xmax is the upper limit parameters (Kim et al, 2003). The chosen level of the parameters with their units and notation are given in Table 2.…”

Section: Finding the Limits Of Process Variablesmentioning

confidence: 99%

Prediction and optimization of weld bead geometry in gas metal arc welding process using RSM and fmincon

Sreeraj¹

2013

J. Mech. Eng. Res.

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“…where X i is the required coded value of parameter, X is any value of parameter from X min − X max , X min is the lower limit of parameters, and X max is the upper limit parameters [4]. The chosen level of the parameters with their units and notation is given in Table 2.…”

Section: Finding the Limits Of Process Variablesmentioning

confidence: 99%

Modelling and Prediction of Stainless Steel Clad Bead Geometry Deposited by GMAW Using Regression and Artificial Neural Network Models

Sreeraj¹,

Kannan

2012

Advances in Mechanical Engineering

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To improve the corrosion-resistant properties of carbon steel, usually cladding process is used. It is a process of depositing a thick layer of corrosion-resistant material over carbon steel plate. Most of the engineering applications require high strength and corrosion resistant materials for long-term reliability and performance. Cladding these properties can be achieved with minimum cost. The main problem faced in cladding is the selection of optimum combinations of process parameters for achieving quality clad and hence good clad bead geometry. This paper highlights an experimental study to predict various input process parameters (welding current, welding speed, gun angle, contact tip-to-work distance, and pinch) to get optimum dilution in stainless steel cladding of low carbon structural steel plates using Gas Metal Arc Welding (GMAW). Experiments were conducted based on central composite rotatable design with full replication technique, and mathematical models were developed using multiple regression method. The developed models have been checked for adequacy and significance. Using Artificial Neural Network (ANN) the parameters were predicted, and percentage of error was calculated between predicted and actual values. The direct and interaction effects of process parameters on clad bead geometry are presented in graphical form.

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“…Kim et al [10] have employed factorial design to correlate the robotic GMAW process parameters (welding voltage, welding speed and arc current) to three responses (bead width, bead height and penetration) for optimization purposes. The material used was plates of AS 1204 mild steel adopting the bead-on-plate technique.…”

Section: Linear Regressionmentioning

confidence: 99%

Optimization of different welding processes using statistical and numerical approaches – A reference guide

Benyounis

Olabi

2008

Advances in Engineering Software

370

128

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Welding input parameters play a very significant role in determining the quality of a weld joint.The joint quality can be defined in terms of properties such as weld-bead geometry, mechanical properties, and distortion. Generally, all welding processes are used with the aim of obtaining a welded joint with the desired weld-bead parameters, excellent mechanical properties with minimum distortion.Nowadays, application of Design of Experiment (DoE), Evolutionary algorithms and computational network are widely used to develop a mathematical relationship between the welding process input parameters and the output variables of the weld joint in order to determine the welding input parameters that lead to the desired weld quality. A comprehensive literature review of the application of these methods in the area of welding has been introduced herein.This review was classified according to the output features of the weld, i.e. bead geometry and mechanical properties of the welds.

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Sensitivity analysis for process parameters in GMA welding processes using a factorial design method

Cited by 148 publications

References 2 publications

Prediction and optimization of weld bead geometry in gas metal arc welding process using RSM and fmincon

Prediction and optimization of weld bead geometry in gas metal arc welding process using RSM and fmincon

Modelling and Prediction of Stainless Steel Clad Bead Geometry Deposited by GMAW Using Regression and Artificial Neural Network Models

Optimization of different welding processes using statistical and numerical approaches – A reference guide

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