ISSN 2360-7955
Abstract:Heat transfer, given its various applications, has long been the focus of researchers and engineers. However, Shock Resistance also takes on a pivotal role in transporting an array of fluids and gases across various industrial domains. This study bridges this discrepancy by scrutinizing the after-effects of a specific non-elastic factor, namely shock resistance, on pipeline weldments and its interaction with elastic properties. This investigation unveils the intricate interrelation, underscoring the necessity of encompassing non-elastic facets 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 (Shock Resistance), thereby enhancing the overall strength and structural integrity of pipeline weldments. The experimental setup adheres to the central composite design, meticulously constructed using design expert software (version 13.0). The response surface methodology analysis yields optimal outcomes, suggesting a current of 160.000 amps, voltage of 21.280 volts, and gas flow rate of 14.667 liters per minute. These parameters collectively yield a welded joint with a shock resistance value of 0.729, 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 study will aid useful knowledge in the development of pipeline weldments that can withstand unexpected impact loads and contribute to the overall sustainability of pipeline systems.
Keywords: Shock Resistance, Structural Integrity, Response surface Methodology, Artificial Neural Network