Modeling and Prediction of Impact Strength for AISI 304l and AISI 316l Stainless Steel Similar and Dissimilar Welded Joints using Regression Analysis and Artificial Neural Networks Approaches

In the present investigation, the AISI 304L and AISI 316L austenitic stainless-steel (ASS) plates were welded together using gas tungsten arc welding (GTAW). Several similar butt joints, typically, 304L-304L and 316L-316L as well as dissimilar butt joints, typically, 304L-316L were formed. The dependance of the impact strength of the welded joints were modeled using regression modelling (RM) and artificial neural network (ANN) approaches. The impact energies of the welded joints were evaluated at ambient and lower temperatures up to -75 oC. The results revealed that, at low temperatures, reducing the temperature reduces the absorbed impact energy of the AISI 304L and 316L similar and dissimilar welded joints. The AISI 304L-316L dissimilar welded joints exhibited higher average impact energies when compared with the similar AISI 304L and AISI 316L welded joints. The generated ANN models based on Multi-Layer Perceptron (MLP) and Radial Basis Function (RBF) approaches can accurately predict the impact energies of the welded joints with a very high accuracy. The RBF and MLP ANN models having layers structure of 2-8-1 and 2-3-1, respectively, showed the best performance among all the investigated networks for predicting the impact energies of welded joints at different temperatures. The mean absolute error (MAE) resulted from the RBF and MLP ANN models are 4.84 and 4.60, respectively.