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RBF-NN-based model for prediction of weld bead geometry in Shielded Metal Arc Welding (SMAW)

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Abstract

Welding processes are considered as an essential component in most of industrial manufacturing and for structural applications. Among the most widely used welding processes is the shielded metal arc welding (SMAW) due to its versatility and simplicity. In fact, the welding process is predominant procedure in the maintenance and repair industry, construction of steel structures and also industrial fabrication. The most important physical characteristics of the weldment are the bead geometry which includes bead height and width and the penetration. Different methods and approaches have been developed to achieve the acceptable values of bead geometry parameters. This study presents artificial intelligence techniques (AIT): For example, radial basis function neural network (RBF-NN) and multilayer perceptron neural network (MLP-NN) models were developed to predict the weld bead geometry. A number of 33 plates of mild steel specimens that have undergone SMAW process are analyzed for their weld bead geometry. The input parameters of the SMAW consist of welding current (A), arc length (mm), welding speed (mm/min), diameter of electrode (mm) and welding gap (mm). The outputs of the AIT models include property parameters, namely penetration, bead width and reinforcement. The results showed outstanding level of accuracy utilizing RBF-NN in simulating the weld geometry and very satisfactorily to predict all parameters in comparison with the MLP-NN model.

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Acknowledgments

This research was supported by the research Grant for the four authors from University Kebangsaan Malaysia DLP-2015-012.

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Authors and Affiliations

  1. Department of Civil Engineering, College of Engineering, Universiti Tenaga Nasional, Kajang, Malaysia

    Ali N. Ahmed

  2. School of Ocean Engineering, Universiti Malaysia Terengganu (UMT), Kuala Terengganu, Malaysia

    C. W. Mohd Noor

  3. Civil and Structural Engineering Department, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi, Malaysia

    Mohammed Falah Allawi

  4. Department of Civil Engineering, Faculty of Engineering, University of Malaya (UM), Kuala Lumpur, Malaysia

    Ahmed El-Shafie

Authors
  1. Ali N. Ahmed

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  2. C. W. Mohd Noor

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  3. Mohammed Falah Allawi

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  4. Ahmed El-Shafie

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Corresponding author

Correspondence toAhmed El-Shafie.

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Ahmed, A.N., Noor, C.W.M., Allawi, M.F.et al. RBF-NN-based model for prediction of weld bead geometry in Shielded Metal Arc Welding (SMAW).Neural Comput & Applic29, 889–899 (2018). https://doi.org/10.1007/s00521-016-2496-0

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