Modelling of Average Weight Loss in Welding Defects using Response Surface Methodology and Artificial Neural Network.

The study aims to bridge this gap by scrutinizing the impact of a specific nonelastic factor, namely the average weight loss, on pipeline weldments and its interaction with elastic properties. To fulfil this objective, a comprehensive experimental inquiry is conducted, encompassing diverse welding methods, materials, and environmental conditions to authentically replicate real-world situations. This investigation unveils the intricate interrelation between elastic and non-elastic facets, underscoring the necessity of encompassing the latter to ensure the dependability of pipeline weldments across various operational contexts. Cutting-edge techniques, such as machine learning algorithms and finite element simulations, are harnessed to accurately predict and optimize these non-elastic factors, thereby enhancing the overall strength and structural integrity of pipeline weldments. The experimental setup adheres to the central composite design, meticulously constructed utilizing design expert software (version 13.0). The response surface methodology analysis yields optimal outcomes, suggesting a gas flow rate of 14.667 liters per minute, a voltage of 21.280 volts, and a current of 160.000 amps. These parameters collectively yield a welded joint with an average weight loss value of 0.236, achieving a desirability value of 0.918. Additionally, the artificial neural network model is employed to predict output parameters and compared against the RSM methodology. The findings underscore the pivotal role of optimizing non-elastic performance factors in pipeline weldments. By accurately anticipating and controlling the period of immersion, engineers and professionals within the pipeline sector can design weldments capable of enduring harsh conditions, thus, prolonging pipeline operational lifespans. [ABSTRACT FROM AUTHOR]