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

Gowtham, K.; Vasudevan, M.; Maduraimuthu, V.; Jayakumar, T.

doi:10.1007/s11663-010-9471-4

Cited by 17 publications

(7 citation statements)

References 15 publications

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“…Most research studies of WBG modeling are focused on consumable electrode welding methods [12], [19]- [23] and flux activated TIG (A-TIG) without deposition [24]- [26]. Only a few study focus on TIG for stainless steel bead geometry [27]- [30], because TIG was traditionally used without deposition or on non-steel metals [31] to weld joints where the reinforcement is not utilized.…”

Section: Introductionmentioning

confidence: 99%

“…Computational intelligence (CI) techniques -such as artificial neural networks (ANN) [12], [22], [23], fuzzy inference systems (FS) [20], [24], [25], evolutionary algorithms (EA) [40], [41], and genetic programming (GP) [42] are widely used to describe the WBG. However, due to their limitations [26], several hybrid computing techniques were developed [26], [32], including the adaptive neurofuzzy inference systems (ANFIS) [25], [26], [43] and the evolutionary fuzzy systems (EFS) [44], [45].…”

Section: Introductionmentioning

confidence: 99%

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Bacterial Memetic Algorithm Trained Fuzzy System-Based Model of Single Weld Bead Geometry

et al. 2020

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This paper presents a fuzzy system-based modeling approach to estimate the weld bead geometry (WBG) from the welding process variables (WPVs) and to achieve a specific weld bead shape. The bacterial memetic algorithm (BMA) is applied to solve these problems in two different roles, as a supervised trainer, and as an optimizer. As a supervised trainer, the BMA is applied to tune two different WBG models. The bead geometry properties (BGP) model follows a traditional approach providing the WBG properties as outputs. The direct profile measurement (DPM) model describes the bead profiles points by a non-linear function realized in the form of fuzzy rules. As an optimizer, the BMA utilizes the developed fuzzy systems to find the solution sets of WPVs to acquire the desired WBG. The best performance is achieved by applying six rules in the BGP model and eleven rules in the DPM model. The results indicate that the normalized root means square error for the validation data set lies in the range of 0.40 − 1.56% for the BGP model and 4.49 − 7.52% for the DPM model. The comparative analysis suggests that the BGP model estimates the BWG in a superior manner when several WPVs are altered. The developed fuzzy systems provide a tool for interpreting the effects of the WPVs. The developed optimizer provides multiple valid set of WPVs to produce the desired WBG, thus supporting the selection of those process variables in applications.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Bacterial Memetic Algorithm Trained Fuzzy System-Based Model of Single Weld Bead Geometry

et al. 2020

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“…The neural network models are developed to predict the weld bead morphology from the infrared (IR) thermal image of the weld pool produced by activated tungsten inert gas (A-TIG) welding of type 316L(N) austenitic stainless steel with minimum root mean square error (RMSE) [8]. The ANFIS is coupled with the GA to optimize the A-TIG welding process parameters for 9Cr-1Mo steel to get a defect-free desired weld bead geometry with small HAZ width [9]. Vishnuvaradhan et al [10] used ANFIS to predict the weld morphology of A-TIG welded nuclear-grade steel.…”

Section: Introductionmentioning

confidence: 99%

Finite element modeling and optimization of hybrid laser–TIG welding of type 316L(N) austenitic stainless steel

Chandrasekhar

Ragavendran

Vasudevan

2020

J Braz. Soc. Mech. Sci. Eng.

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An integrated approach was followed for determining the optimized process parameters for hybrid laser-tungsten inert gas (TIG) welding of 316L(N) stainless steel which involve FEM followed by optimization. FEM-based model was developed to predict the thermal distribution for a various combination of welding process parameters. The hybrid heat source combining conical and double-ellipsoidal heat sources was used for generating the temperature distribution during welding. The penetration depth produced by welding was estimated from the thermal model for various set of process parameters by running the model several times. The generated dataset consisting of process parameters and the depth of penetration (DOP) was used for developing adaptive neuro-fuzzy information system (ANFIS) model for correlating the set of input process parameters with the output DOP. This ANFIS model was then used in the objective function of a genetic algorithm (GA) for identifying the optimum process parameters to achieve the maximum DOP during autogenous hybrid laser-TIG welding. The optimized solutions were validated by performing bead-on-plate experiments on 5.6-mm-thick type 316L(N) stainless steel plates. The above-integrated approach was found to accurately determine the optimum hybrid laser-TIG welding process parameters for achieving the maximum DOP with minimum experimentation.

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“…Kovacevic and Zhang [20] have experimentally modeled weld pool geometry using Neurofuzzy. Vasudevan et al [21][22][23] have used hybrid techniques along with GA to optimize process parameters for GTAW of austenitic Stainless steels. De and Bag [24] have coupled GA with heat transfer model to predict process variables in GTA spot welding.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Intelligent Modeling for Optimizing Welding Process Parameters for Reduced Activation Ferritic-Martensitic (RAFM) Steel

Neelamegam¹,

Sapineni

Muthukumaran³

et al. 2013

JILSA

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Reduced-activated ferritic-martensitic steels are being considered for use in fusion energy reactor and subsequent fusion power reactor applications. Typically, those reduced activated steels can loose their radioactivity in approximately 100 years, compared to thousands of years for the non-reduced-activated steels. The commonly used welding process for fabricating this steel are electron-beam welding, and tungsten inert gas (TIG) welding. Therefore, Activated-flux tungsten inert gas (A-TIG) welding, a variant of TIG welding has been developed in-house to increase the depth of penetration in single pass welding. In structural materials produced by A-TIG welding process, weld bead width, depth of penetration and heat affected zone (HAZ) width play an important role in determining in mechanical properties and also the performance of the weld joints during service. To obtain the desired weld bead geometry, HAZ width and make a good weld joint, it becomes important to set up the welding process parameters. The current work attempts to develop independent models correlating the welding process parameters like current, voltage and torch speed with weld bead shape will bead shape parameters like depth of penetration, bead width, HAZ width using ANFIS. These models will be used to evaluate the objective function in the genetic algorithm. Then 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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Intelligent Modeling Combining Adaptive Neuro Fuzzy Inference System and Genetic Algorithm for Optimizing Welding Process Parameters

Cited by 17 publications

References 15 publications

Bacterial Memetic Algorithm Trained Fuzzy System-Based Model of Single Weld Bead Geometry

Bacterial Memetic Algorithm Trained Fuzzy System-Based Model of Single Weld Bead Geometry

Finite element modeling and optimization of hybrid laser–TIG welding of type 316L(N) austenitic stainless steel

Hybrid Intelligent Modeling for Optimizing Welding Process Parameters for Reduced Activation Ferritic-Martensitic (RAFM) Steel

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