RESUMO
In this article, an attempt was made to join DP600 steel and Ti6Al4V titanium alloy sheets by resistance spot-welding (RSW) using an interlayer in the form of Cu and Au layers fabricated through the cold-spraying process. The welded joints obtained by RSW without an interlayer were also considered. The influence of Cu and Au as an interlayer on the resulting microstructure as well as mechanical properties (shear force and microhardness) of the joints were determined. A typical type of failure of Ti6Al4V/DP600 joints produced without the use of an interlayer is brittle fracture. The microstructure of the resulting joint consisted mainly of the intermetallic phases FeTi and Fe2Ti. The microstructure of the Ti6Al4V/Au/DP600 joint contained the intermetallic phases Ti3Au, TiAu, and TiAu4. The intermetallic phases TiCu and FeCu were found in the microstructure of the Ti6Al4V/Cu/DP600 joint. The maximum tensile/shear stress was 109.46 MPa, which is more than three times higher than for a welded joint fabricated without the use of Cu or Au interlayers. It has been observed that some alloying elements, such as Fe, can lower the martensitic transformation temperature, and some, such as Au, can increase the martensitic transformation temperature.
RESUMO
The automobile industry relies primarily on spot welding operations, particularly resistance spot welding (RSW). The performance and durability of the resistance spot-welded joints are significantly impacted by the welding quality outputs, such as the shear force, nugget diameter, failure mode, and the hardness of the welded joints. In light of this, the present study sought to determine how the aforementioned welding quality outputs of 0.5 and 1 mm thick austenitic stainless steel AISI 304 were affected by RSW parameters, such as welding current, welding time, pressure, holding time, squeezing time, and pulse welding. In order to guarantee precise evaluation and experimental analysis, it is essential that they are supported by a numerical model using an intelligent model. The primary objective of this research is to develop and enhance an intelligent model employing artificial neural network (ANN) models. This model aims to provide deeper knowledge of how the RSW parameters affect the quality of optimum joint behavior. The proposed neural network (NN) models were executed using different ANN structures with various training and transfer functions based on the feedforward backpropagation approach to find the optimal model. The performance of the ANN models was evaluated in accordance with validation metrics, like the mean squared error (MSE) and correlation coefficient (R2). Assessing the experimental findings revealed the maximum shear force and nugget diameter emerged to be 8.6 kN and 5.4 mm for the case of 1-1 mm, 3.298 kN and 4.1 mm for the case of 0.5-0.5 mm, and 4.031 kN and 4.9 mm for the case of 0.5-1 mm. Based on the results of the Pareto charts generated by the Minitab program, the most important parameter for the 1-1 mm case was the welding current; for the 0.5-0.5 mm case, it was pulse welding; and for the 0.5-1 mm case, it was holding time. When looking at the hardness results, it is clear that the nugget zone is much higher than the heat-affected zone (HZ) and base metal (BM) in all three cases. The ANN models showed that the one-output shear force model gave the best prediction, relating to the highest R and the lowest MSE compared to the one-output nugget diameter model and two-output structure. However, the Levenberg-Marquardt backpropagation (Trainlm) training function with the log sigmoid transfer function recorded the best prediction results of both ANN structures.