The monitoring weld quality is increasingly important because great financial savings are possible because of it, and this especially happens in manufacturing where defective welds lead to losses in production and necessitate time consuming and expensive repair. This research deals with the monitoring and controllability of the fusion arc welding process using Artificial Neural Network (ANN) model. The effect of weld parameters on the weld quality was studied by implementing the experimental results obtained from welding a non-Galvanized steel plate ASTM BN 1323 of 6 mm thickness in different weld parameters (current, voltage, and travel speed) monitored by electronic systems that are followed by destructive (Tensile and Bending) and non-destructive (Hardness on HAZ) tests to investigate the quality control on the weld specimens. The experimental results obtained are then processed through the ANN model to control the welding process and predict the level of quality for different welding conditions. It has been deduced that the welding conditions (current, voltage, and travel speed) have a dominant factors that affect the weld quality and strength. Also we found that for certain welding condition, there was an optimum weld travel speed to obtain an optimum weld quality. The system supports quality control procedures and welding productivity without doing more periodic destructive mechanical test to dozens of samples.
