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

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dc.citedby33
dc.contributor.authorAhmed A.N.en_US
dc.contributor.authorNoor C.W.M.en_US
dc.contributor.authorAllawi M.F.en_US
dc.contributor.authorEl-Shafie A.en_US
dc.contributor.authorid57214837520en_US
dc.contributor.authorid55956848000en_US
dc.contributor.authorid57057678400en_US
dc.contributor.authorid16068189400en_US
dc.date.accessioned2023-05-29T06:53:10Z
dc.date.available2023-05-29T06:53:10Z
dc.date.issued2018
dc.descriptionArtificial 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 weldingen_US
dc.description.abstractWelding 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.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1007/s00521-016-2496-0
dc.identifier.epage899
dc.identifier.issue3
dc.identifier.scopus2-s2.0-84979702687
dc.identifier.spage889
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84979702687&doi=10.1007%2fs00521-016-2496-0&partnerID=40&md5=1755a07974ac520a68bc83ff99d0512f
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/23919
dc.identifier.volume29
dc.publisherSpringer Londonen_US
dc.sourceScopus
dc.sourcetitleNeural Computing and Applications
dc.titleRBF-NN-based model for prediction of weld bead geometry in Shielded Metal Arc Welding (SMAW)en_US
dc.typeArticleen_US
dspace.entity.typePublication
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