Artificial neural networks as an aid to steel plate distortion reduction

Lightfoot, M. P.; McPherson, Norman; Woods, K.; Bruce, George

doi:10.1016/j.jmatprotec.2005.10.023

Cited by 18 publications

(10 citation statements)

References 5 publications

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“…This architecture has been previously used successfully in simulations predicting both welding distortion and weld geometry [11,12,20]. The development of the model to predict the distortion rectification due to the travelling induction coil was carried out in 3 distinct stages.…”

Section: Ann Model Developmentmentioning

confidence: 99%

“…In particular welding speed has been shown to have a significant impact on distortion as it is one of the primary parameters of controlling heat input during welding [9]. Computational modelling methods such as Finite Element Analysis and Artificial Neural Networks have previously been implemented to predict the out-of-plane distortion experienced by a structure due to the welding process and have shown the ability to predict the final distortion with a reasonable degree of accuracy [4,12].…”

Section: Introductionmentioning

confidence: 99%

“…When supplied with sufficient training data, ANN models are capable of capturing intricate relationships between parameters and detecting subtle trends that other computational approaches may not. These characteristics make ANN models ideally suited to modelling the welding process with its many closely linked parameters and they have been successfully implemented to predict both weld geometry and welding distortion [11,12,20].…”

mentioning

confidence: 99%

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Artificial neural network prediction of weld distortion rectification using a travelling induction coil

Barclay

Campbell

Galloway

et al. 2013

Int J Adv Manuf Technol

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An experimental investigation has been carried out to determine the applicability of an induction heating process with a travelling induction coil for the rectification of angular welding distortion. The results obtained from experimentation have been used to create artificial neural network models with the ability to predict the welding induced distortion and the distortion rectification achieved using a travelling induction coil.The experimental results have shown the ability to reduce the angular distortion for 8 mm and 10 mm thick DH36 steel plate and effectively eliminate the distortion on 6 mm thick plate. Results for 6 mm plate also show the existence of a critical induction coil travel speed at which maximum corrective bending occurs.Artificial neural networks have demonstrated the ability to predict the final distortion of the plate after both welding and induction heating. The models have also been used as a tool to determine the optimum speed to minimise the resulting distortion of steel plate after being subjected to both welding and induction heating processes.

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Section: Ann Model Developmentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Artificial neural network prediction of weld distortion rectification using a travelling induction coil

Barclay

Campbell

Galloway

et al. 2013

Int J Adv Manuf Technol

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show abstract

“…A large number of experiments have been performed to examine and quantify the variability of post weld distortion profiles of plates welded under similar conditions, as reported in e.g. [12]. However, since there are a number of factors that influence the final distortion, e.g.…”

Section: Introductionmentioning

confidence: 99%

The Influence of Pre-existing Deformation on GMA Welding Distortion in Thin Steel Plates

Davies

Ahn

Tsunori

et al. 2014

J. of Materi Eng and Perform

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Weld distortion is particularly problematic for large thin structures that are used in the assembly of ships. The drive towards lighter ships and thinner plate is restricted by the significant increase in distortion as the plate thickness decreases. The influence of pre-existing deformation in the plates to be joined on the resultant distortion in gas metal arc (GMA) welded structure has been studied. DH-36 steel plate surface profiles were measured before and after the butt welding of two plates 1000 × 500 × 4 mm in size. Three dimensional finite element (FE) models that incorporate the initial plate profile have been created to simulate the welding process and to examine the relationship between the final welded plate profiles and the initial deformation present in the plates.Both symmetric and asymmetric models were considered. A significant variation in the unwelded base plates' initial distortion was observed. Generally, it has been found that if an out-of-plane deformation exists in a plate prior to welding, the level of distortion further increases in the same direction following welding. The final distortions are strongly related to the initial plate profiles. The residual stress distributions in the plates are also to some extent affected by the level of distortion initially present.

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“…Therefore, there has always been a need for a more efficient method for determination of such stresses in terms of input welding parameters. Neural networks offers an alternative to standard computer techniques in control technology, and has attracted a wide interest and applications [11][12][13][14]. The advantage of the neural networks is that the network can be updated continuously with new data to improve and optimize its performance.…”

Section: Introductionmentioning

confidence: 99%

Prediction of residual stresses in gas arc welding by back propagation neural network

Ahmadzadeh

Fard

Saranjam

et al. 2012

NDT & E International

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Artificial neural networks as an aid to steel plate distortion reduction

Cited by 18 publications

References 5 publications

Artificial neural network prediction of weld distortion rectification using a travelling induction coil

Artificial neural network prediction of weld distortion rectification using a travelling induction coil

The Influence of Pre-existing Deformation on GMA Welding Distortion in Thin Steel Plates

Prediction of residual stresses in gas arc welding by back propagation neural network

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