Publication: RBF-NN-based model for prediction of weld bead geometry in Shielded Metal Arc Welding (SMAW)
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Date
2018
Authors
Ahmed A.N.
Noor C.W.M.
Allawi M.F.
El-Shafie A.
Journal Title
Journal ISSN
Volume Title
Publisher
Springer London
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. � 2016, The Natural Computing Applications Forum.
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