A study on relationship between process variables and bead penetration for robotic CO2 arc welding

Kim, I.S; Son, Joon-Sik; Kim, I.G; Kim, J.Y; Kim, O.S

doi:10.1016/s0924-0136(02)01126-3

Cited by 93 publications

(45 citation statements)

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“…First, adjustable welding current, arc voltage and welding speed. Second, torch angle, free wire length, nozzle distance, weld direction, weld position and flow rate of gas [5,6]. For enough penetration and right welding profile occurrence, the quality of welding joints are affected by welding current, arc voltage, welding speed and gas shielding parameters [2,[4][5].…”

Section: Introductionmentioning

confidence: 99%

The Effects of GMAW Parameters on Penetration, Hardness and Microstructure of AS3678-A350 High Strength Steel

Kaewsakul¹,

Putrontaraj²,

Kimapong³

2015

The International Journal of Advanced Culture Technology

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Section: Introductionmentioning

confidence: 99%

The Effects of GMAW Parameters on Penetration, Hardness and Microstructure of AS3678-A350 High Strength Steel

Kaewsakul¹,

Putrontaraj²,

Kimapong³

2015

The International Journal of Advanced Culture Technology

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“…J.S. Son et al have predicted the weld bead geometry and bead penetration in Shielded Metal Arc Welding (SMAW) process by neural network [17]. P.K.…”

Section: Review Of Traditional Methodsmentioning

confidence: 99%

Weld Quality Prediction Using Artificial Intelligence Technique

Ojha¹,

Yadav²,

Yadav

2014

SMSJ

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“…Arti cial Neural Networks (ANNs) are widely proposed in the literature as mathematical tools to implement the estimation methods needed in processes of this kind, because of its learning capability [5][6][7][8].…”

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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A study on relationship between process variables and bead penetration for robotic CO2 arc welding

Cited by 93 publications

References 0 publications

The Effects of GMAW Parameters on Penetration, Hardness and Microstructure of AS3678-A350 High Strength Steel

The Effects of GMAW Parameters on Penetration, Hardness and Microstructure of AS3678-A350 High Strength Steel

Weld Quality Prediction Using Artificial Intelligence Technique

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

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