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

Artificial intelligence; Carbon steel; Electric arc welding; Forecasting; Gas metal arc welding; Geometry; Neural networks; Radial basis function networks; Repair; Steel research; Welding; Welds; Artificial intelligence techniques; Multilayer perceptron neural networks; Physical characteristics; Pre...

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Main Authors: Ahmed A.N., Noor C.W.M., Allawi M.F., El-Shafie A.
Other Authors: 57214837520
Format: Article
Published: Springer London 2023
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spelling my.uniten.dspace-239192023-05-29T14:53:10Z RBF-NN-based model for prediction of weld bead geometry in Shielded Metal Arc Welding (SMAW) Ahmed A.N. Noor C.W.M. Allawi M.F. El-Shafie A. 57214837520 55956848000 57057678400 16068189400 Artificial intelligence; Carbon steel; Electric arc welding; Forecasting; Gas metal arc welding; Geometry; Neural networks; Radial basis function networks; Repair; Steel research; Welding; Welds; Artificial intelligence techniques; Multilayer perceptron neural networks; Physical characteristics; Prediction model; Radial basis function neural networks; RBF-NN; Shielded metal arc welding; Welding process; Electric welding 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. � 2016, The Natural Computing Applications Forum. Final 2023-05-29T06:53:10Z 2023-05-29T06:53:10Z 2018 Article 10.1007/s00521-016-2496-0 2-s2.0-84979702687 https://www.scopus.com/inward/record.uri?eid=2-s2.0-84979702687&doi=10.1007%2fs00521-016-2496-0&partnerID=40&md5=1755a07974ac520a68bc83ff99d0512f https://irepository.uniten.edu.my/handle/123456789/23919 29 3 889 899 Springer London Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
description Artificial intelligence; Carbon steel; Electric arc welding; Forecasting; Gas metal arc welding; Geometry; Neural networks; Radial basis function networks; Repair; Steel research; Welding; Welds; Artificial intelligence techniques; Multilayer perceptron neural networks; Physical characteristics; Prediction model; Radial basis function neural networks; RBF-NN; Shielded metal arc welding; Welding process; Electric welding
author2 57214837520
author_facet 57214837520
Ahmed A.N.
Noor C.W.M.
Allawi M.F.
El-Shafie A.
format Article
author Ahmed A.N.
Noor C.W.M.
Allawi M.F.
El-Shafie A.
spellingShingle Ahmed A.N.
Noor C.W.M.
Allawi M.F.
El-Shafie A.
RBF-NN-based model for prediction of weld bead geometry in Shielded Metal Arc Welding (SMAW)
author_sort Ahmed A.N.
title RBF-NN-based model for prediction of weld bead geometry in Shielded Metal Arc Welding (SMAW)
title_short RBF-NN-based model for prediction of weld bead geometry in Shielded Metal Arc Welding (SMAW)
title_full RBF-NN-based model for prediction of weld bead geometry in Shielded Metal Arc Welding (SMAW)
title_fullStr RBF-NN-based model for prediction of weld bead geometry in Shielded Metal Arc Welding (SMAW)
title_full_unstemmed RBF-NN-based model for prediction of weld bead geometry in Shielded Metal Arc Welding (SMAW)
title_sort rbf-nn-based model for prediction of weld bead geometry in shielded metal arc welding (smaw)
publisher Springer London
publishDate 2023
_version_ 1806423548246360064
score 13.214268

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