GMAW welding optimization using genetic algorithms

Correia, D.; Gonçalves, Cristiene Vasconcelos; Sebastião, S. C.; Ferraresi, Valtair Antonio

doi:10.1590/s1678-58782004000100005

Cited by 51 publications

(27 citation statements)

References 3 publications

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“…The study by Sathiya et al 7 exposed an overall idea of the optimization of friction welding parameters using different techniques. Correia et al 8 proposed a method to decide near-optimal settings of a GMAW welding process. Near-best values of three control variables (welding voltage, wire feed rate and welding speed) based on four quality responses (deposition efficiency, bead width, depth of penetration and reinforcement), inside a previous delimited experimental region were chosen.…”

Section: Weldability Studies and Parameter Optimization Of Aisi 904lmentioning

confidence: 99%

Weldability studies and parameter optimization of AISI 904L super austenitic stainless steel using friction welding

et al. 2014

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Welding input parameters play a very significant role in determining the quality of a weld joint. The quality of the joint can be defined in terms of mechanical properties, distortion and weld-bead geometry. Generally, all welding processes are employed with the aim of obtaining a welded joint with the desired characteristics. The purpose of this study is to propose a method to decide near optimal settings for the welding process parameters in friction welding of (AISI 904L) super austenitic stainless steel by using non conventional techniques and genetic algorithm (GA). Grey relational analysis and the desirability approach were applied to optimize the input parameters by considering multiple output variables simultaneously. An optimization method based on genetic algorithm was then applied to resolve the mathematical model and to select the optimum welding parameters. The main objective of this work is to determine the friction welding process parameters to maximize the fatigue life and minimize the width of the partial deformation zone (left & right) and welding time. This study describes how to obtain near optimal welding conditions over a wide search space by conducting relatively a smaller number of experiments. The optimized values obtained through these evolutionary computational techniques were also compared with experimental results. ANOVA analysis was carried out to identify the significant factors affecting fatigue strength, welding time and partially deformed zone and to validate the optimized parameters.

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Section: Weldability Studies and Parameter Optimization Of Aisi 904lmentioning

confidence: 99%

Weldability studies and parameter optimization of AISI 904L super austenitic stainless steel using friction welding

et al. 2014

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“…To overcome this problem, the response surface methodology uses the near-optimal values as a reference point to obtain a model of the welding process and determine optimal values of the process variables. Correia et al (2004) adopted a similar approach, where a GA was used as a tool to decide near-optimal settings of a GMAW process. The search for the near-optimal settings was carried out step by step with the help of a GA predicting the next experiment based on the previous, and without using the knowledge of the modeling equations between the inputs and outputs of the GMAW process.…”

Section: Literature Surveymentioning

confidence: 99%

Optimization of bead geometry in electron beam welding using a Genetic Algorithm

Dey

Pratihar

Datta

et al. 2009

Journal of Materials Processing Technology

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“…Correia et al [6] investigated the implementation possibility of using GA as a method to decide near optimal settings of a GMAW process and compared its performance with RSM. Kim et al [21] suggested a GA and RSM combined approach for determining optimal welding conditions.…”

Section: Introductionmentioning

confidence: 99%

Process parameter optimization of lap joint fillet weld based on FEM–RSM–GA integration technique

Islam

Buijk²,

Rais‐Rohani

et al. 2015

Advances in Engineering Software

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a b s t r a c tThis study introduces a welding process design tool to determine optimal arc welding process parameters based on Finite Element Method (FEM), Response Surface Method (RSM) and Genetic Algorithms (GA). Here, a sequentially integrated FEM-RSM-GA framework has been developed and implemented to reduce the weld induced distortion in the final welded structure. It efficiently incorporates finite element based numerical welding simulations to investigate the desired responses and the effect of design variables without expensive trial experiments. To demonstrate the effectiveness of the proposed methodology, a lap joint fillet weld specimen has been used in this paper. Four process parameters namely arc voltage, input current, welding speed and welding direction have been optimized to minimize the distortion of the structure. The optimization results revealed the effectiveness of the methodology for welding process design with reduced cost and time.

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GMAW welding optimization using genetic algorithms

Cited by 51 publications

References 3 publications

Weldability studies and parameter optimization of AISI 904L super austenitic stainless steel using friction welding

Weldability studies and parameter optimization of AISI 904L super austenitic stainless steel using friction welding

Optimization of bead geometry in electron beam welding using a Genetic Algorithm

Process parameter optimization of lap joint fillet weld based on FEM–RSM–GA integration technique

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