The comprehensive intention of this paper is to evaluate the optimal welding parameters for joining two dissimilar materials by friction stir welding (FSW) process which is termed as a green manufacturing technology in order to generate quality joints. Conventionally, the optimization of process parameters experimentally is carried out by a time-consuming trial and error technique. Also, the effect of two or more parameters cannot be considered at the same time experimentally. Due to this, mathematical modelling is carried out to determine the optimal welding parameters. This paper focuses on a theory that hybridizes the exploring capability of non-dominated sorting genetic algorithm-II (NSGA-II) and exploitation capability of Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method. The performance parameters of FSW process has a high level of nonlinearity due to which artificial neural network (ANN) is employed to mathematically model the performance measures. Full factorial design is employed to plan the experimentations. At random, ten dataset were used for testing the ANN structures and the remaining data were used to train the network for predicting the ultimate tensile strength (UTS), hardness and impact energy. The optimal networks had root mean square error (RMSE) and mean absolute error (MAE) of 0.7486 and 0.0074, 0.4045 and 0.003 and 0.1866 and 0.0354 respectively. The transfer equations developed from the ANN models is used as the fitness function for the NSGA-II algorithm. The optimal welding parameters obtained from the proposed hybrid algorithm are Tool rotation speed = 1693rpm, Traverse speed= 2.72 mm/s and Copper as advancing side material. Validation of the optimal result is done by carrying out experiments that are conducted at the simulated optimal parameter. Finally, a macro and microstructural study of the welded joint at the simulated optimal parameter is carried out in order to assess the behaviour of the weld.

本文的主要目的是评估通过摩擦搅拌焊接（FSW）工艺连接两种异种材料的最佳焊接参数，该工艺被称为绿色制造技术以产生高质量的接头。传统上，通过耗时的反复试验技术来实验性地优化工艺参数。同样，不能同时通过实验考虑两个或多个参数的影响。因此，要进行数学建模以确定最佳的焊接参数。本文着眼于将非支配排序遗传算法-II（NSGA-II）的探索能力与“基于理想解的相似性优先排序技术”（TOPSIS）方法的开发能力相混合的理论。FSW过程的性能参数具有高度的非线性，这是由于采用了人工神经网络（ANN）对性能指标进行数学建模。采用全因子设计来计划实验。随机使用十个数据集测试ANN结构，其余数据用于训练网络以预测极限抗拉强度（UTS），硬度和冲击能。最佳网络的均方根误差（RMSE）和平均绝对误差（MAE）分别为0.7486和0.0074、0.4045和0.003和0.1866和0.0354。由ANN模型开发的传递方程用作NSGA-II算法的适应度函数。从提出的混合算法获得的最佳焊接参数为：工具转速= 1693rpm，横向速度= 2。72毫米/秒，铜为前进侧材料。最佳结果的验证是通过执行在模拟的最佳参数上进行的实验来完成的。最后，在模拟的最佳参数下对焊接接头进行了宏观和微观结构研究，以评估焊接性能。