Optimization of underwater wet welding process parameters using neural network

Abstract

Background: The structural integrity of welds carried out in underwater wet environment is very key to the reliability of welded structures in the offshore environment. The soundness of a weld can be predicted from the weld bead geometry. Methods: This paper illustrates the application of artificial neural network approach in the optimization of the welding process parameter and the influence of the water environment. Neural network learning algorithm is the method used to control the welding current, voltage, contact tube-to-work distance, and speed so as to alter the influence of the water depth and water environment. Results: The result of this work gives a clear insight of achieving proper weld bead width (W), penetration (P), and reinforcement (R).002. Conclusions: An interesting implication of this work is that it will lead to a robust welding activity so as to achieve sound welds for offshore construction industries.