Genetic algorithm for optimization of welding variables for height to width ratio and application of ANN for prediction of bead geometry for TIG welding process

Nagesh, D. S.; Datta, G. L.

doi:10.1016/j.asoc.2009.10.007

Cited by 120 publications

(45 citation statements)

References 17 publications

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“…Nagesh, et.al [7], clarified an incorporated strategy with another methodology utilizing test outline lattice of test plans system on the exploratory information accessible from ordinary experimentation, use of neural system for foreseeing the weld globule geometric descriptors and utilization of hereditary calculation for advancement of procedure parameters. An endeavor has been made to foresee the dot shape parameters utilizing backengendering neural system.…”

Section: Existing Research Effortsmentioning

confidence: 99%

A Review on Parameters Optimization of Tungsten Inert Gas Welding using Taguchi

2017

IJSR

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The main objective of industries reveal with producing better quality product at minimum cost and increase productivity. In the present paper an attempt is made to understand the effect of TIG welding parameters such as welding current , gas flow rate ,welding speed that are influences on responsive output parameters such as hardness of welding, tensile strength of welding, by using optimization philosophy.

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Section: Existing Research Effortsmentioning

confidence: 99%

A Review on Parameters Optimization of Tungsten Inert Gas Welding using Taguchi

2017

IJSR

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“…Multi-Layer Perceptron (MLP) is one of the most commonly used ANN structures because of its capability to solve non-linearly separable classi cation problems and to approximate continuous functions [16,25]. A supervised way in the training stage has been used for tuning of its weights and biases, providing a set of pairs of input-output values, which allow the MLP to learn the relations between the input and output variables [25].…”

Section: Bpnn Network Developingmentioning

confidence: 99%

“…A good agreement between the optimized values obtained from this technique and experimental results has been shown. An ANN-PSO algorithm for simulation and optimization of WBG of 316L nickel based super alloys in ux cored arc welding process has been proposed by Katherasan et al [16]. ANN has been used for modeling of the process; then, the developed model has been embedded into a PSO algorithm, which optimizes the process parameters.…”

Section: Introductionmentioning

confidence: 99%

Simultaneous Optimization of Joint Edge Geometry and Process Parameters in Gas Metal Arc Welding Using Integrated ANN - PSO Approach

2017

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Abstract. Gas Metal Arc Welding (GMAW) is one of the most extensively used processes in automated welding due to its high productivity. However, to simultaneously achieve several con icting objectives such as reducing production time, increasing product quality, full penetration, proper joint edge geometry, and optimal selection of process parameters, a multi-criteria optimization procedure must be used. The aim of this research is to develop a multi-criteria modeling and optimization procedure for GMAW process. To simultaneously predict Weld Bead Geometry (WBG) characteristics and Heat-A ected Zone (HAZ), a Back Propagation Neural Network (BPNN) has been proposed. The experimentally derived data sets are used in training and testing of the network. Results demonstrate that the nely tuned BPNN model can closely simulate actual GMAW process with less than 1% error. Next, to simultaneously optimize process characteristics, the BPNN model is inserted into a Particle Swarm Optimization (PSO) algorithm. The proposed technique determines a set of values for parameters and the workpiece groove angle in such a way that a pre-speci ed WBG is achieved while the HAZ of the weld joint is minimized. Optimal results are veri ed through additional experiments.

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“…Advances in Artificial Neural Systems [22] 2008 RSM RSM Graphical Chang [5] 2008 Taguchi ANN SA Chatsirirungruang and Miyakawa [13] 2009 Taguchi Taguchi GA Cheng et al [12] 2 0 0 2 R S M A N N G A Cojocaru et al [23] 2009 Full factorial MLR Graphically Martinez Delfa et al [24] 2009 RSM RSM, ANN Mathematically Mukherjee and Ray [10] 2008 N/A ANN Modified TS Nagesh and Datta [25] 2010 Fractional factorial design MLR, ANN GA Noorossana et al [11] 2008 RSM ANN, FS GA Pasandideh and Niaki [9] 2 0 0 6 R S M R S M G A Patnaik and Biswas [26] 2007 Taguchi Taguchi (S/N) Weighting Pizarro et al [27] 2006 Taguchi RSM Graphically where the parameters and in the formulas are convexity coefficients and specify how strictly the target value will be desired. In the current study, and are equal to one.…”

Section: Desirability Functions Formula With Different Objectsmentioning

confidence: 99%

Desirability Improvement of Committee Machine to Solve Multiple Response Optimization Problems

Golestaneh

Ismail

Ariffin

et al. 2013

Advances in Artificial Neural Systems

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Multiple response optimization (MRO) problems are usually solved in three phases that include experiment design, modeling, and optimization. Committee machine (CM) as a set of some experts such as some artificial neural networks (ANNs) is used for modeling phase. Also, the optimization phase is done with different optimization techniques such as genetic algorithm (GA). The current paper is a development of recent authors' work on application of CM in MRO problem solving. In the modeling phase, the CM weights are determined with GA in which its fitness function is minimizing the RMSE. Then, in the optimization phase, the GA specifies the final response with the object to maximize the global desirability. Due to the fact that GA has a stochastic nature, it usually finds the response points near to optimum. Therefore, the performance the algorithm for several times will yield different responses with different GD values. This study includes a committee machine with four different ANNs. The algorithm was implemented on five case studies and the results represent for selected cases, when number of performances is equal to five, increasing in maximum GD with respect to average value of GD will be eleven percent. Increasing repeat number from five to forty-five will raise the maximum GD by only about three percent more. Consequently, the economic run number of the algorithm is five.

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Genetic algorithm for optimization of welding variables for height to width ratio and application of ANN for prediction of bead geometry for TIG welding process

Cited by 120 publications

References 17 publications

A Review on Parameters Optimization of Tungsten Inert Gas Welding using Taguchi

A Review on Parameters Optimization of Tungsten Inert Gas Welding using Taguchi

Simultaneous Optimization of Joint Edge Geometry and Process Parameters in Gas Metal Arc Welding Using Integrated ANN - PSO Approach

Desirability Improvement of Committee Machine to Solve Multiple Response Optimization Problems

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