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Received: 12 October 2020 Accepted: 23 February 2021 Based on high quality and reliability, one of the most efficient methods for joining metals is Submerged Arc Welding (SAW). In this presented work, an attempt has been successfully taken to develop a model to predict the effect of input parameters on weld bead geometry of submerged arc welding process with the help of neural network technique and analysis of various process control variables and important of weld bead parameters in submerged arc welding. The complexity non-linear relationships of input / output variables for any computational models can be addressed by using artificial neural networks (ANN). Today, ANN represents a powerful modeling technique, that depend on statistical approach, presently practiced in many fields of engineering for modeling complex relationships that other physical models cannot be explained it easily. A welding process with automatic or semiautomatic is required to complete the weld through using tubular electrode with consumable flux. Parameters such as welding current, welding speed and voltage are influenced on the quality of the joints. The work conducts many experiments; these are basically depending on many factors and levels. A selection of 2205 duplex stainless steel is carried out in this study to conduct three factors and five levels of central composite design. Neural network model structure having number of neurons layers such as (3 input layers, 1 hidden layer and 3 output layers) with back propagation algorithm has been successfully applied to extract weld bead geometry from predicting the effect of input parameters. Good agreement was obtained between predicted and experiment results, however process parameters such as speed shows opposite effect on all weld parameters. It was seen that weld height and width are proportional to the amount of input current. The prediction of the neural network model showed excellent agreement with the actual results, which indicate that the neural network is viable means for predicting of not only weld bead geometry, but also other parameters such as polarity, current type and flux geometry. This recommends setting the neural network to be applicable for real time work.

Using Artificial Neural Networks to Predict the Effect of Input Parameters on Weld Bead Geometry for SAW Process