RBF-NN-based model for prediction of weld bead geometry in Shielded Metal Arc Welding (SMAW)
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 pro...
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my.um.eprints.226282019-09-30T08:21:53Z http://eprints.um.edu.my/22628/ RBF-NN-based model for prediction of weld bead geometry in Shielded Metal Arc Welding (SMAW) Ahmed, Ali Najah Noor, Che Wan Mohd Allawi, Mohammed Falah El-Shafie, Ahmed TA Engineering (General). Civil engineering (General) 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. Springer Verlag (Germany) 2018 Article PeerReviewed Ahmed, Ali Najah and Noor, Che Wan Mohd and Allawi, Mohammed Falah and El-Shafie, Ahmed (2018) RBF-NN-based model for prediction of weld bead geometry in Shielded Metal Arc Welding (SMAW). Neural Computing and Applications, 29 (3). pp. 889-899. ISSN 0941-0643 https://doi.org/10.1007/s00521-016-2496-0 doi:10.1007/s00521-016-2496-0 |
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TA Engineering (General). Civil engineering (General) Ahmed, Ali Najah Noor, Che Wan Mohd Allawi, Mohammed Falah El-Shafie, Ahmed RBF-NN-based model for prediction of weld bead geometry in Shielded Metal Arc Welding (SMAW) |
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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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Article |
author |
Ahmed, Ali Najah Noor, Che Wan Mohd Allawi, Mohammed Falah El-Shafie, Ahmed |
author_facet |
Ahmed, Ali Najah Noor, Che Wan Mohd Allawi, Mohammed Falah El-Shafie, Ahmed |
author_sort |
Ahmed, Ali Najah |
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 Verlag (Germany) |
publishDate |
2018 |
url |
http://eprints.um.edu.my/22628/ https://doi.org/10.1007/s00521-016-2496-0 |
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1646210294515499008 |
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13.159267 |