ABSTRACT

Ever-growing needs in pipeline welding relating to quality, frequent usage of advanced, heat-sensitive, and corrosion-resistant materials alongside high production costs encourage the evaluation of a welding process thoroughly. The only way to overcome those problems is the adaptation of technically evolved welding methods that combine the goodness of cutting-edge power supply sources and advanced software configuration. One such technique is Regulated Metal Deposition (RMD^TM) welding. In this context, the current study explores the influence of the regulated metal deposition (RMD^TM) welding variables on ASME SA387-11-2 steels. Current (A), voltage (V), and gas flow rate (GFR) are preferred regulated metal deposition (RMD™) welding variables, and at the same time the depth of penetration (DOP), heat-affected zone (HAZ), bead height (BH), and bead width (BW) are incorporated as performance evaluation attributes. Furthermore, the exploration of Rao algorithms is presented to evaluate the optimal welding settings. The obtained optimal welding settings is current = 92 A, voltage = 13 V, and gas flow rate = 21 litre/min. The results are also compared with the JAYA, and teaching learning-based optimization (TLBO) approaches to show the efficacy of the intended approach.

摘要

与质量相关的管道焊接需求不断增长，对先进、热敏和耐腐蚀材料的频繁使用以及高生产成本促使对焊接工艺进行彻底评估。克服这些问题的唯一方法是采用技术发展的焊接方法，将尖端电源的优点和先进的软件配置相结合。一种这样的技术是调节金属沉积（RMD ^TM）焊接。在此背景下，目前的研究探讨了调节金属沉积 (RMD ^TM) ASME SA387-11-2 钢的焊接变量。电流 (A)、电压 (V) 和气体流速 (GFR) 是首选的调节金属沉积 (RMD™) 焊接变量，同时熔深 (DOP)、热影响区 (HAZ)、焊缝高度 (BH) 和焊缝宽度 (BW) 被纳入作为性能评估属性。此外，还介绍了 Rao 算法的探索，以评估最佳焊接设置。获得的最佳焊接设置为电流 = 92 A，电压 = 13 V，气体流速 = 21 升/分钟。还将结果与 JAYA 和基于学习的优化 (TLBO) 教学方法进行比较，以显示预期方法的有效性。