Prediction of weld bead geometry and penetration in shielded metal-arc welding using artificial neural networks

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

doi:10.1016/s0924-0136(02)00101-2

Cited by 239 publications

(111 citation statements)

References 2 publications

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“…In this work, the 25 sample points were randomly divided into five groups, and each of them consists five sample points. The order of groups was G k = {(13, 23, 22, 2, 3) , (24,18,16,6,21), (20,12,5,10,9), (11,7,1,4,14), (25,8,19,17,15)}, where the number corresponded to the No. in Table 2.…”

Section: Prediction Performance Of the Stochastic Kriging Modelmentioning

confidence: 99%

“…Gunaraj et al [8] applied a response surface methodology (RSM) to devise a four-factor five-level central composite rotatable design matrix to fabricate pipes of different specifications in submerged arc welding. Nagesh et al [9] explored the connection between the shielded metal-arc welding process parameters and the characteristics of the welding bead and penetration utilizing artificial neural networks model. Srivastava et al [10] employed polynomial response surface (PRS) model to study how the gas metal arc WPP influenced welding quality.…”

Section: Introductionmentioning

confidence: 99%

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Accurate Prediction of the Weld Bead Characteristic in Laser Keyhole Welding Based on the Stochastic Kriging Model

Ruan

Zhou

Shu

et al. 2018

Metals

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Abstract:As an important index of weld quality, the weld bead geometry is closely related to the welding process parameters (WPP). Therefore, it is crucial to establish the relationships between the WPP and weld bead shape to serve as an indicator of the weld quality. However, it is difficult to predict the weld bead shape accurately due to uncertainty. In this paper, laser keyhole welding (LKW) experiments are conducted on 2205 stainless steel at sample points generated by the optimal Latin hypercube sampling (OLHS). Then the relationships between WPP and weld width (WW) are constructed using stochastic kriging model (SKM), considering the randomness of the welding process. To verify the effectiveness of the SKM, two validation approaches, the additional experiments validation and k-fold cross-validation, are used to compare the prediction performance of SKM and the traditional kriging model. SKM is superior to the kriging model at the whole five additional test points with smaller relative error. As to k-fold cross-validation, SKM provides a smaller root mean square error at four in five groups of the data. In addition, SKM can provide the variations of the entire weld bead shape. Overall, the SKM is very prominent in predicting the weld bead shape, considering fluctuations of WPP.

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Section: Prediction Performance Of the Stochastic Kriging Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Accurate Prediction of the Weld Bead Characteristic in Laser Keyhole Welding Based on the Stochastic Kriging Model

Ruan

Zhou

Shu

et al. 2018

Metals

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“…Usage of ANN to model the GMAW process was reported by Nagesh and Datta [31]. BPN was used to associate the welding process parameters (electrode feed rate, arc power, arc voltage, arc current and arc length) with output features of bead geometry (bead height and width, penetration depth and area).…”

Section: Artificial Neural Network (Anns)mentioning

confidence: 99%

Optimization of different welding processes using statistical and numerical approaches – A reference guide

Benyounis

Olabi

2008

Advances in Engineering Software

370

128

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Welding input parameters play a very significant role in determining the quality of a weld joint.The joint quality can be defined in terms of properties such as weld-bead geometry, mechanical properties, and distortion. Generally, all welding processes are used with the aim of obtaining a welded joint with the desired weld-bead parameters, excellent mechanical properties with minimum distortion.Nowadays, application of Design of Experiment (DoE), Evolutionary algorithms and computational network are widely used to develop a mathematical relationship between the welding process input parameters and the output variables of the weld joint in order to determine the welding input parameters that lead to the desired weld quality. A comprehensive literature review of the application of these methods in the area of welding has been introduced herein.This review was classified according to the output features of the weld, i.e. bead geometry and mechanical properties of the welds.

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“…The models were used to predict penetration, width, and e ective throat thickness under a set of weld parameters and alternate frequency of shielding gas. A procedure based on ANNs for modelling of GMAW parameters has been proposed by Ates [12]. Mechanical properties of the weld joint such as tensile strength, impact strength, elongation, and weld metal hardness have been modeled and predicted using the proposed ANN.…”

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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Prediction of weld bead geometry and penetration in shielded metal-arc welding using artificial neural networks

Cited by 239 publications

References 2 publications

Accurate Prediction of the Weld Bead Characteristic in Laser Keyhole Welding Based on the Stochastic Kriging Model

Accurate Prediction of the Weld Bead Characteristic in Laser Keyhole Welding Based on the Stochastic Kriging Model

Optimization of different welding processes using statistical and numerical approaches – A reference guide

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

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