Soft computing methods used for the modelling and optimisation of Gas Metal Arc Welding: a review

Pal, Kamal; Pal, Surjya K.

doi:10.1504/ijmr.2011.037911

Cited by 19 publications

(2 citation statements)

References 56 publications

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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%

A Fuzzy Rule for Improving the Performance of Multiobjective Job Dispatching in a Wafer Fabrication Factory

Chen¹,

Wang

2013

Journal of Applied Mathematics

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This paper proposes a fuzzy slack-diversifying fluctuation-smoothing rule to enhance the scheduling performance in a wafer fabrication factory. The proposed rule considers the uncertainty in the remaining cycle time and is aimed at simultaneous improvement of the average cycle time, cycle time standard deviation, the maximum lateness, and number of tardy jobs. Existing publications rarely discusse ways to optimize all of these at the same time. An important input to the proposed rule is the job remaining cycle time. To this end, this paper proposes a self-adjusted fuzzy back propagation network (SA-FBPN) approach to estimate the remaining cycle time of a job. In addition, a systematic procedure is also established, which can solve the problem of slack overlapping in a nonsubjective way and optimize the overall scheduling performance. The simulation study provides evidence that the proposed rule can improve the four performance measures simultaneously.

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Section: Step 1: Estimating the Remaining Cycle Timementioning

confidence: 99%

A Fuzzy Rule for Improving the Performance of Multiobjective Job Dispatching in a Wafer Fabrication Factory

Chen¹,

Wang

2013

Journal of Applied Mathematics

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“…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%

Application of Multiple Kernel Support Vector Regression for Weld Bead Geometry Prediction in Robotic GMAWProcess

Mollayi¹,

Eidi²

2018

IJECE

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<p><span>Modelling and prediction of weld bead geometry is an important issue in robotic GMAW process. This process is highly non-linear and coupled multivariable system and the relationship between process parameters and weld bead geometry cannot be defined by an explicit mathematical expression. Therefore, application of supervised learning algorithms can be useful for this purpose. Support vector machine is a very successful approach to supervised learning. In this approach, a higher degree of accuracy and generalization capability can be obtained by using the multiple kernel learning framework, which is considered as a great advantage in prediction of weld bead geometry due to the high degree of prediction accuracy required. In this paper, a novel approach for modelling and prediction of the weld bead geometry, based on multiple kernel support vector regression analysis has been proposed, which benefits from a high degree of accuracy and generalization capability. This model can be used for proper selection of welding parameters in order to obtain a desired weld bead geometry in robotic GMAW process.</span></p>

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Industry 4.0 in Welding

Mishra

Pal

Chakravarty

2021

Materials Forming, Machining and Tribology

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Soft computing methods used for the modelling and optimisation of Gas Metal Arc Welding: a review

Cited by 19 publications

References 56 publications

A Fuzzy Rule for Improving the Performance of Multiobjective Job Dispatching in a Wafer Fabrication Factory

A Fuzzy Rule for Improving the Performance of Multiobjective Job Dispatching in a Wafer Fabrication Factory

Application of Multiple Kernel Support Vector Regression for Weld Bead Geometry Prediction in Robotic GMAWProcess

Industry 4.0 in Welding

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