SS 201 had been reported as a good substitute for SS304 without any significant compromise in performance. However, modeling EBW process using an efficient tool like adaptive neuro-fuzzy inference system (ANFIS) and use of multi-objective optimization to optimize its performance are not reported yet. Thus, the present study employed ANFIS models tuned by genetic algorithm, particle swarm optimization, gray wolf optimizer, and bonobo optimizer (BO) to predict weld attributes during EBW of SS201 as a function of input process parameters. Among the developed models, ANFIS tuned by BO was seen to yield the best prediction accuracy. In multi-objective optimization (MOO), the two conflicting goals were to minimize secondary dendritic arm spacing and maximize Vicker’s hardness number simultaneously. In MOO, some interesting facts were observed, such as the fixed input parameter of power (P) as 3200-W and squeezed experimental range for the welding speed (S).

据报道，SS 201是SS304的良好替代品，而性能没有任何重大损害。但是，尚未报道使用诸如自适应神经模糊推理系统（ANFIS）之类的有效工具对EBW过程进行建模以及使用多目标优化来优化其性能。因此，本研究采用遗传算法，粒子群优化，灰狼优化器和bo黑猩猩优化器（BO）进行优化的ANFIS模型，以预测SS201的EBW期间焊接属性作为输入工艺参数的函数。在已开发的模型中，可以看到BO调谐的ANFIS具有最佳的预测精度。在多目标优化（MOO）中，两个相互矛盾的目标是最小化次生树突臂间距并同时最大化维氏硬度值。在MOO中，观察到一些有趣的事实，