Abstract
The manufacturing of structures ranging from bridges and machinery to all types of seaborne vehicles to nuclear reactors and space rockets has made considerable use of arc welding technologies. This is as a result of benefits including increased joint efficiency, air and water tightness, no thickness restriction (0.6 to 25 mm), decreased fabrication time and cost, etc. when compared to alternative fabrication methods. Gas metal arc welding (GMAW) is a frequently used welding technology in industries due to its inherent benefits, including deeper penetration, a smooth bead, etc. Local heating and cooling that takes place during the multi-pass welding process causes complicated stresses to develop at the weld zone, which ultimately causes angular distortion in the weldment. Angular distortion is a major flaw that affects the weld's properties as well as the cracking and misalignment of the welded joints. The issue of angular distortion can be successfully solved by predicting it in relation to certain GMAW process variables. A neural network model was created in this research to predict angular distortion. A fractional factorial approach with 125 runs was used to conduct the exploratory experiments. A neural network model with feed forward and backward propagation was developed using the experimental data. To train the neural network model, the Levenberg-Marquardt method was utilised. The results indicate that the model based on network 4-9-3 is more effective in forecasting angular distortion with time gaps between two, three, and four passes than the other three networks (4-2-3, 4-4-3, 797 and 4-8-3). Prediction accuracy is more than 95 percent. The neural network model developed in this study can be used to manage the welding cycle in structural steel weld plates to achieve the best possible weld quality with the least amount of angular distortion.