Global Journal of Environmental Science and Technology

ISSN 2360-7955

Analytical Modelling of Hardness as a non-elastic performance factor in augmenting the Structural Integrity of Pipeline Weldment

Abstract:Pipeline welds' structural integrity and strength serve as a cornerstone for ensuring operational safety and effectiveness. Although substantial research has delved into optimizing mechanical properties like yield strength and Young's modulus, a notable gap exists concerning the prediction and enhancement of non-elastic performance factors that substantially influence durability and long-term performance. This research aims to close this gap by scrutinizing the impact of a specific non-elastic factor, namely the Brinell hardness on pipeline weldments. To fulfil this objective, a comprehensive experimental inquiry is conducted, encompassing diverse welding methods, materials, and environmental conditions to authentically replicate real-world situations. 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 hardness of 216.414mpa, 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, in which the RSM in this case had better predicted values. The findings underscore the pivotal role of optimizing non-elastic performance factors in pipeline weldments. By accurately anticipating and controlling the hardness, engineers and professionals within the pipeline sector can design weldments capable of enduring harsh conditions, curbing the risk of failures, and significantly prolonging pipeline operational lifespans.


Keywords: Hardness, Mechanical properties, Response Surface Methodology, Artificial Neural Network.. Post-harvest loss in tomatoes and tomatillos is a major problem in the market supply chain of small farm holders.