PREDICTION OF WELD BEAD GEOMETRY OF SQUARE BUTT JOINT WITH ARC ROTATION IN PULSED GMA WELDING USING NEURAL NETWORK

摘要

In this investigation, four input process parameters viz. arc rotational speed, eccentricity, wire feed rate and ratio of wire feed rate to welding speed and output parameters viz. bead height, bead width and depth of penetration have been studied. The experiments were conducted on square butt joint plate of 5083-H111 aluminum alloy with arc rotation using full factorial design. The back-propagation neural network has been applied for predicting the weld bead geometry. Accuracy analysis also has been performed to find the percentage error in predicting the weld bead geometry. It is observed that back-propagation neural network can predict these output parameters with good accuracy.