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Because more than 90% of the domestic manufacturing processes for the Liquefied Natural Gas (LNG) storage tanks rely on welding and processing technologies, the advancement of welding and processing technologies is directly connected to the productivity and therefore the advancement is critical to be competitive in the domestic shipbuilding industry. The welding technology using a laser light source is a more advanced technology than conventional arc welding in terms of workability, precision, and productivity. Although its application area is currently limited, this technology has been emerging as an important assembly tool in the manufacturing process of shipbuilding and offshore structures in the future. Because the LNG storage tank is a cryogenic structure, 9% nickel steel is widely used to manufacture the tank for both room temperature and low-temperature environments due to its excellent mechanical properties and fatigue strength. In terms of strength, 9% Ni steel is equivalent to 680 MPa-level high-tensile strength steel, and is usually used in applications where the operating temperature is below-150℃, such as LNG tanks with QT treatment. The 9% Ni steel has higher strength and better weldability than A5083-O aluminum alloy, has better impact toughness at cryogenic temperatures than SUS304L, and is economic. Therefore, 9% Ni steel is widely used to manufacture LNG tanks. Previous studies on the 9% Ni steel are based on butt welding, and research has been conducted according to the welding process. However, because 30-40% of LNG storage tanks are formed in a curved shape, research on the fillet welding process to overcome the limitations of butt welding has not been actively conducted to date. More specifically, research on the development of an algorithm for setting process variables, which is the core technology of fillet welding, needs to be conducted. Therefore, in this study, fiber laser welding, which is a fillet shape, is studied and performed using 9% Ni steel. The main objective of this study is to optimize the welding process variables by predicting weld properties. To derive the optimal process variables, the GBO algorithm was developed based on mathematical models. Finally, the developed algorithm showed an average error rate of 0.01831%, which ensures the high reliability of the optimal process variables.

Purpose: Since the welding automations have widely been required for industries and engineering, the development of the predicted model has become more important for the increased demands for the automatic welding systems where a poor welding quality becomes apparent if the welding parameters are not controlled. The automated welding system must be modelling and controlling the changes in weld characteristics and produced the output that is in some way related to the change being detected as welding quality. To be acceptable a weld quality must be positioned accurately with respect to the joints, have good appearance with sufficient penetration and reduce low porosity and inclusion content. Design/methodology/approach: To achieve the objectives, two intelligent models involving the use of a neural network algorithm in arc welding process with the help of a numerical analysis program MATLAB have been developed. Findings: The results represented that welding quality was fully capable of quantifying and qualifying the welding faults. Research limitations/implications: Welding parameters in the arc welding process should be well established and categorized for development of the automatic welding system. Furthermore, typical characteristics of welding quality are the bead geometry, composition, microstructure and appearance. However, an intelligent algorithm that predicts the optimal bead geometry and accomplishes the desired mechanical properties of the weldment in the robotic GMA (Gas Metal Arc) welding should be required. The developed algorithm should expand a wide range of material thicknesses and be applicable in all welding position for arc welding process. Furthermore, the model must be available in the form of mathematical equations for the automatic welding system. Practical implications: The neural network models which called BP (Back Propagation) and LM (Levenberg-Marquardt) neural networks to predict optimal welding parameters on the required bead reinforcement area in lab joint in the robotic GMA welding process have been developed. Experimental results have been employed to find the optimal algorithm to predict bead reinforcement area by BP and LM neural networks in lab joint in the robotic GMA welding. The developed intelligent models can be estimated the optimal welding parameters on the desired bead reinforcement area and weld criteria, establish guidelines and criteria for the most effective joint design for the robotic arc welding process. Originality/value: In this study, intelligent models, which employed the neural network algorithms, one of AI (Artificial Intelligence) technologies have been developed to study the effects of welding parameters on bead reinforcement area and to predict the optimal bead reinforcement area for lab joint in the robotic GMA welding process. BP (Back Propagation) and LM (Levenberg-Marquardt) neural network algorithm have been used to develop the intelligent model.

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