In this research work, aluminium with low-carbon steel was joined through the explosive welding process at different loading ratios. The high impact pressure caused by explosive energy at the weld interface results in increase in hardness value. This hardness property plays an important role, as it affects the mechanical properties of welded plates. A model based on an intelligent technique named adaptive network-based fuzzy inference system (ANFIS) has been developed to predict the various micro-hardness values across the weld interface of explosive welded plates. The obtained experimental data were utilized for training and testing of the ANFIS model to predict micro-hardness values. The developed model was used on both sides of the weld interface, i.e. aluminium and steel. The model performance evaluations were carried out using different statistical criteria such as cross-correlation, mean absolute percentage error (MAPE) and root-mean-square error (RMSE). In comparison with aluminium, the steel side showed good results with a value of adj. R-square (0.95955) when compared to that in aluminium (0.85343). This observation was also supported by MAPE and RMSE data. The experimentally obtained micro-hardness values were found to be in good agreement with predicted ones through the ANFIS model.

在这项研究工作中，通过爆炸焊接工艺以不同的负载率将铝和低碳钢结合在一起。在焊接界面处由爆炸能量引起的高冲击压力导致硬度值增加。这种硬度特性起着重要的作用，因为它会影响焊接板的机械性能。已经开发了一种基于智能技术的模型，该模型称为基于自适应网络的模糊推理系统（ANFIS），以预测爆炸焊接板的整个焊接界面的各种显微硬度值。将获得的实验数据用于ANFIS模型的训练和测试，以预测显微硬度值。开发的模型用于焊接界面的两侧，即铝和钢。使用不同的统计标准（例如互相关，平均绝对百分比误差（MAPE）和均方根误差（RMSE））进行模型性能评估。与铝相比，钢面显示出了很好的效果，调整值为adj。与铝（0.85343）相比，R平方（0.95955）。MAPE和RMSE数据也支持该观察。通过ANFIS模型发现，实验获得的显微硬度值与预测值相符。