2011
DOI: 10.1504/ijmr.2011.037911
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Soft computing methods used for the modelling and optimisation of Gas Metal Arc Welding: a review

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Cited by 19 publications
(2 citation statements)
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“…Past studies (e.g., [14]) have shown that the accuracy of remaining cycle time estimation can be improved by job classification. Soft computing methods (e.g., [16]) have received much attention in this field.…”
Section: Step 1: Estimating the Remaining Cycle Timementioning
confidence: 99%
“…Past studies (e.g., [14]) have shown that the accuracy of remaining cycle time estimation can be improved by job classification. Soft computing methods (e.g., [16]) have received much attention in this field.…”
Section: Step 1: Estimating the Remaining Cycle Timementioning
confidence: 99%
“…The algorithms in this field can be used to establish a model based on a limited set of observations for making predictions in cases which have not observed. Therefore, these algorithms can be used for modelling and prediction of weld geometry in GMAW process and several researches have been performed in this field which are mainly based on neural networks and fuzzy systems [6]. In field robotic GMAW process, a global database of process parameters and the corresponding weld geometry has been provided by [7] and predictive modelling has been performed by both the neural network and second order regression analysis methods, which proves the higher accuracy of theneural networkapproach over the second order regression.…”
Section: Introductionmentioning
confidence: 99%