Journal of Welding Science

This research examines the microstructure and microhardness in the additive manufacturing process using the laser metal deposition method with the deposition of Inconel 718 powder on the Inconel 738 substrate. For this purpose, deposition with different laser power was performed on different substrates, and the microstructure and hardness of the layers were studied. Three layers of Inconel 718 powder were deposited on the substrates. The results show that the laser power parameter in the deposition process significantly affects the microstructure of the samples. By increasing the laser power by 100 W, the distance between the phases γ' in the substrate and γ'' in the layers decreased significantly. With increasing laser power, an increase in the geometric dimensions and volume percentage of the γ'' phase was also observed. In addition, increasing the laser power decreased the volume percentage of the Laves phase. By measuring the microhardness of the deposition layers, it was found that the hardness of the third layer decreases with increasing laser power.
 

In Wire and arc additive manufacturing (WAAM) based on Gas metal arc welding (GMAW) is one of the methods of manufacturing metal layer by layer. One of this method's basic steps is predicting the welding geometry created in each welding step. In the current research, an experimental study was conducted in this field considering the effective parameters of welding geometry. For this purpose, three parameters of voltage, welding speed, and wire feeding speed were considered as effective parameters on the welding geometry of the process. The width and height of the weld bead was selected as the answer according to the type and application of the research. The least squares support vector machine was used to model the welding geometry in the process. The results obtained from the regression (R2) of train, test, validation, and total were 0.945, 0.793, 0.894, and 0.881 respectively. The comparison between the experimental data and the model data shows the significance of the proposed model.

Society's great and growing demand for buildings and structures has created the need to apply new construction methods to shorten construction times, make buildings lighter, extend their useful life, and make them more earthquake-proof. In the long term, the new methods will lead to structural optimization, increased building performance, and the achievement of optimal operating conditions. New technologies are meeting society's increasing need for special structures more than ever. Additive manufacturing is based on gas metal arc welding as one of the fastest and most cost-effective manufacturing methods for primary metal structures. For this purpose, the three parameters voltage, wire feed speed, and welding speed were considered initial parameters affecting the width and height of the welding flux. To investigate the effects of the process,

16 experiments with input parameters were evaluated. The width and height of the sweat pollen were determined by experimental investigations. Subsequently, the resulting welding geometry is modeled using three numerical modeling methods, including intensive learning machines, relevence vector machine, and fuzzy logic. The comparison between the experimental data and the results of the three generated models shows that fuzzy logic comes closest to the experimental data of the welding geometry of the modeling methods. For example, the test data of the generative fuzzy model resulted in an average error for height and width of 0.667 and 0.5477, respectively, and a root mean square error for height and width of 0.0046 and 0.3, respectively, which expresses the generalization ability and reliability compared to other modeling methods in this process. Finally, a metal pattern of a special structure was produced based on arc and wire additive manufacturing based gas metal arc welding.