Estimation and optimization of depth of penetration in hybrid CO2 LASER-MIG welding using ANN-optimization hybrid model

Ghosal, S.; Chaki, Sudipto

doi:10.1007/s00170-009-2234-1

Cited by 47 publications

(16 citation statements)

References 20 publications

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“…The welding current, arc voltage, welding speed, heat input rate are chosen as welding parameters. The effect of these parameters on the size of heat affected zone is investigated [8].…”

Section: International Journal Of Advances In Scientificmentioning

confidence: 99%

Effect of Process Parameters on Weldability of the Material

2017

IJASRE

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In this study, the effect of various welding process parameters on the weld ability of Mild Steel specimens having dimensions 50mm× 50mm × 6 mm welded by metal arc welding will be investigated. The welding current, arc voltage, welding speed, heat input rate are chosen as welding parameters. The depth of penetrations are to be calculated for each specimen after the welding operation on closed butt joint and the effects of welding speed and heat input rate parameters on depth of penetration will be estimated and then investigated by applying optimization and regression modelling

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“…The welding current, arc voltage, welding speed, heat input rate are chosen as welding parameters. The effect of these parameters on the size of heat affected zone is investigated [8].…”

Section: International Journal Of Advances In Scientificmentioning

confidence: 99%

Effect of Process Parameters on Weldability of the Material

2017

IJASRE

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“…Taguchi-based grey relational analysis method was successfully applied to optimize the input parameters in dissimilar friction stir butt welding of aluminum alloys (Kasman 2013). Ghosal and Chaki (2010) used a hybrid artificial neural network (ANN) technique to optimize the depth of penetration in CO 2 -laser/MIG welding. Sait et al (2009) used the Taguchi method in combination with desirability function to optimize machining parameters of glass fibrereinforced plastic.…”

Section: Literature Reviewmentioning

confidence: 99%

Multi Objective Optimization of Flux Cored Arc Weld Parameters Using Hybrid Grey - Fuzzy Technique

Satheesh

Dhas

2014

Jou. Eng. Res.

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Abstract:In the present work, an attempt has been made to use the grey-based fuzzy logic method to solve correlated multiple response optimization problems in the field of flux cored arc welding. This approach converts the complex multiple objectives into a single grey-fuzzy reasoning grade. Based on the grey-fuzzy reasoning grade, optimum parameters are identified. The significant contributions of parameters are estimated using analysis of variance (ANOVA). This evaluation procedure can be used in intelligent decision making for a welding operator. The proposed and developed method has good accuracy and competency. The proposed technique provides manufacturers who develop intelligent manufacturing systems a method to facilitate the achievement of the highest level of automation.

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“…Researchers have already employed GA with a user-defined objective function for optimization of the process parameters in laser cutting [24], laser welding [25], laser forming [26], and in other advanced machining processes including ultrasonic machining (USM), abrasive jet machining (AJM), water jet machining (WJM) and abrasive-water jet machining (AWJM) [27]. Ghosal and Chaki [28] have employed an ANN-Quasi Newton hybrid model for estimation and optimization of depth of penetration in hybrid CO 2 laser-MIG welding. Recently, Kumar et al [29] have employed an integrated GA-Taguchi model for parametric optimization of the submerged arc welding process.…”

Section: Introductionmentioning

confidence: 99%

A Ga–ann Hybrid Model for Prediction and Optimization of Co2 Laser-Mig Hybrid Welding Process

Chaki¹,

Ghosal²

2015

Int. J. Automot. Mech. Eng.

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The paper presents a hybrid model of an Artificial Neural Network (ANN) and Genetic Algorithm (GA) for modeling of a hybrid laser welding process. This model is employed for the prediction and optimization of penetration depth with corresponding process parameters. A single program developed for the purpose initially establishes an optimized ANN architecture using a Back-Propagation Neural Network (BPNN), with Bayesian regularization (BR). This trained ANN is then used in conjunction with GA to find optimum parameters for the process. An experimental dataset obtained from published literature has been employed to study the effect of process input parameters (arc power, focal distance from the workpiece surface, torch angle and the distance between the laser and the welding torch.) in CO 2 laser-MIG hybrid welding on the depth of penetration for 5005 Al-Mg alloy, and has been used in the present work for the purpose of training, testing and optimization of the GA-ANN model. The results indicate that the GA-ANN model can predict the output with reasonably good accuracy (mean absolute % errors of 0.7198%) and can optimize the process parameters with a negligible computational time of 100.09 s. The proposed approach is envisaged for application in a multi-variable complex problem with reasonable accuracy for prediction and optimization of operational parameters. A 4-7-1 network trained using BPNN with the BR method has been found to show the best prediction capability and a maximum penetration depth of 3.84 mm has been obtained during optimization.

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Estimation and optimization of depth of penetration in hybrid CO2 LASER-MIG welding using ANN-optimization hybrid model

Cited by 47 publications

References 20 publications

Effect of Process Parameters on Weldability of the Material

Effect of Process Parameters on Weldability of the Material

Multi Objective Optimization of Flux Cored Arc Weld Parameters Using Hybrid Grey - Fuzzy Technique

A Ga–ann Hybrid Model for Prediction and Optimization of Co2 Laser-Mig Hybrid Welding Process

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