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

Abstract

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.

Prediction of Weld Strength in Power Ultrasonic Spot Welding Process Using Artificial Neural Network (ANN) and Back Propagation Method

“…The extended of using Taguchi method with ANN is done on study and optimize the selected parameters proposed for turning machine, which the investigation of parameters is obtained by employing array in orthogonal manner, aspect ration of signal with respect to noise also use the variance to characterise the removal steel bar by carbide tool (Yang & Tarng, 1998). A contribution study on the effect of input parameters on quality of weld bead geometry for submerged arc welding is carried out based on ANN with back propagation method, the data are successfully trained for the network structure of neurons layers to permit for predicting bead geometry and to reduce the error percentage of multiple tests, which the results show the viability of using ANN not only for prediction weld quality but also to be an efficient method for real time work (Saeed & Al Sarraf, 2021). In addition, an investigation of welding parameters is done on welded specimens made from (ABS) and (PMMA) polymers by ultrasonic welding; the strength of weld is predicted using ANN technique.…”

“…As the ANN technique is considered to be a powerful technique in determining a proper correlation between process parameter and output variables, this technique has also able to recognize linear and non linear models and find a relationship for the selecting data based on the concept of learn. This advantageous make ANN to be simple, effective and low cost (Saeed & Al Sarraf, 2021). The current work has performed a back propagation (BP) combined with ANN to dominant the forward and backward passes for learning algorithm.…”