Genetic-Algorithm-Based Computational Models for Optimizing the Process Parameters of A-TIG Welding to Achieve Target Bead Geometry in Type 304 L(N) and 316 L(N) Stainless Steels

Vasudevan, M.; Bhaduri, A.K.; Raj, Baldev; Rao, Kalvala Prasad

doi:10.1080/10426910701323342

Cited by 72 publications

(38 citation statements)

References 17 publications

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“…Based on the error values in the predicted weld bead parameters, the crossover rate was fixed at 0.76, implying that crossover was carried out only on 76 chromosomes among the 100 chromosomes, and the remaining chromosomes were carried over to the next generation without any alteration. [22] After the crossover, mutation was carried out on the offsprings in which one allele of the gene is randomly replaced by another to produce a new genetic structure. The mutation probability is kept low at a rate of 0.001 to avoid any possible perturbations.…”

Section: Selection Of Genetic Algorithm Parametersmentioning

confidence: 99%

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Intelligent Modeling Combining Adaptive Neuro Fuzzy Inference System and Genetic Algorithm for Optimizing Welding Process Parameters

Gowtham

Vasudevan

Maduraimuthu

et al. 2011

Metall Mater Trans B

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Modified 9Cr-1Mo ferritic steel is used as a structural material for steam generator components of power plants. Generally, tungsten inert gas (TIG) welding is preferred for welding of these steels in which the depth of penetration achievable during autogenous welding is limited. Therefore, activated flux TIG (A-TIG) welding, a novel welding technique, has been developed in-house to increase the depth of penetration. In modified 9Cr-1Mo steel joints produced by the A-TIG welding process, weld bead width, depth of penetration, and heat-affected zone (HAZ) width play an important role in determining the mechanical properties as well as the performance of the weld joints during service. To obtain the desired weld bead geometry and HAZ width, it becomes important to set the welding process parameters. In this work, adaptative neuro fuzzy inference system is used to develop independent models correlating the welding process parameters like current, voltage, and torch speed with weld bead shape parameters like depth of penetration, bead width, and HAZ width. Then a genetic algorithm is employed to determine the optimum A-TIG welding process parameters to obtain the desired weld bead shape parameters and HAZ width.

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Section: Selection Of Genetic Algorithm Parametersmentioning

confidence: 99%

“…Similar observation has been reported earlier. [22] For the purpose of experimentally validating the model, a set of process parameters is considered. The target and the actual depth of penetration, bead width, and HAZ width using the multiobjective GA model are given in Table IV.…”

Section: E Validation Of Genetic Algorithm Modelmentioning

confidence: 99%

Intelligent Modeling Combining Adaptive Neuro Fuzzy Inference System and Genetic Algorithm for Optimizing Welding Process Parameters

Gowtham

Vasudevan

Maduraimuthu

et al. 2011

Metall Mater Trans B

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“…The above optimization problem was solved using Quasi-Newton method. Vasudevan et al (2007) used a Genetic Algorithm (GA) to achieve the target bead geometry in Tungsten Inert Gas welding by optimizing the process parameters.…”

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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“…The basic principles of GA were first laid down rigorously by Holland, 19 and are well described in many texts like Deb's 20 . In GA, the initial population is the possible solution to the optimization problem and each possible solution is called an individual 10 . Each individual is represented as a binary string consisting of combinations of randomly generated 0s and 1s 21 .…”

Section: Development Of Mathematical Modelsmentioning

confidence: 99%

“…A binary‐coded GA with a penalty term was used to solve the said problem 9 . Vasudevan et al have developed; GA based computational models to determine the optimum/near‐optimum process parameters to achieve the target weld‐bead geometry in 304LN and 316LN stainless steel welds produced by a TIG welding 10 . Kumaran et al validated the application of GA to friction welding of tube‐to‐tube plate using an external tool process by means of computing the deviation between predicted and experimentally obtained welding process parameters 11…”

Section: Introductionmentioning

confidence: 99%

Optimization of GMAW Process Parameters in Austenitic Stainless Steel Cladding Using Genetic Algorithm Based Computational Models

Yoganandh

Kannan²,

Babu

et al. 2012

Experimental Techniques

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Weld cladding is a process of depositing a thick layer of anticorrosive metal on a corrosive substrate surface to impart better corrosion resistance properties. In weld cladding process, the difficulty generally encountered will be the problem of selecting optimum combination of input process parameters to achieve the desired dilution level. Until recently trial and error methods were employed to determine the optimum process parameters, which result in wastage of cost and time. This paper focuses on optimization of GMAW process parameters, which are used for deposition of austenitic stainless steel on low carbon structural steel plates. Experiments were conducted based on four‐factor five‐level central composite rotatable design with full replication technique. Mathematical models relating to gas metal arc welding process parameters to clad bead geometry were developed using multiple regression method. Developed mathematical models are helpful in predicting the clad bead geometry and in setting process parameters at optimum values to accomplish the desirable dilution level at relatively low cost with a high degree of reproducibility. The accuracy of the results was tested by conducting conformity tests using the same experimental set up. Moreover, Genetic Algorithm tool with GUI available in MATLAB 7.0 was used to optimize the process parameters to achieve optimum dilution.

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Genetic-Algorithm-Based Computational Models for Optimizing the Process Parameters of A-TIG Welding to Achieve Target Bead Geometry in Type 304 L(N) and 316 L(N) Stainless Steels

Cited by 72 publications

References 17 publications

Intelligent Modeling Combining Adaptive Neuro Fuzzy Inference System and Genetic Algorithm for Optimizing Welding Process Parameters

Intelligent Modeling Combining Adaptive Neuro Fuzzy Inference System and Genetic Algorithm for Optimizing Welding Process Parameters

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

Optimization of GMAW Process Parameters in Austenitic Stainless Steel Cladding Using Genetic Algorithm Based Computational Models

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