Prediction of the primary dendritic arm spacing in the laser metal deposition of Inconel 718 superalloy using the numerical and experimental techniques

Abstract

In order to obtain the knowledge about the formation of the oriented structure in the additive manufacturing process, it is required to gain an accurate understanding about the formation and growth of grains. Hence, in this paper, the simulation of laser metal deposition was performed by the volume of fluid technique to predict the primary dendritic arm spacing, fluid flows, and geometry of the deposited layer. Moreover, the laser metal deposition of the Inconel 718 superalloy was performed experimentally to verify the results of simulation. From the results of simulation, a maximum error of about 8% was observed in the prediction of the geometry of the deposited layer. In addition, the difference between the value of primary dendritic arm spacing in the experimental and simulation results was about 14%. The results have also shown that a cellular dendritic structure was formed when the ratio of the temperature gradient to the cooling rate was more than 9 × 108, while when this ratio was less than 9 × 108, a columnar dendritic structure was formed. The results of simulations and experiments showed that an increase in the laser power from 350 to 450 W led to an improvement of about 9.5% in the size of primary dendrite arms due to the reduction in the temperature gradient, while the increase in the scanning speed from 3 to 5 mm/s resulted in a reduction of about 21% in the size of primary dendrite arms due to the faster cooling rate.

Sintering parameter optimization by inverse analysis in direct metal deposition of Inconel 718

“…As can be seen from Figure 6, the maximum measured temperature without powder injection is about 70°C and with powder injection is about 50°C. The low temperature in this small distance from the surface (2 mm) is due to the low dimensions of the molten pool, which is also reported in the numerical simulation results (Afshari et al. , 2023c; Afshari et al.…”

“…Therefore, the best choice is numerical simulation of the DMD process with a validated model. The designed model should consider mass, momentum and energy transfer relationships as well as physical phenomena during the additive manufacturing process to predict the behavior of the microstructure during solidification (Afshari et al, 2023b;Afshari et al, 2023c). Using this model, it is possible to predict the maximum pool temperature, fluid velocity distribution in the pool, molten pool boundary, solidification geometry, solidification microstructure, solidification volume change, residual stress, distortion and changes in some mechanical properties.…”

“…The designed model should consider mass, momentum and energy transfer relationships as well as physical phenomena during the additive manufacturing process to predict the behavior of the microstructure during solidification (Afshari et al. , 2023b; Afshari et al. , 2023c).…”

Evaluation of the Effect of Process Parameters on Mass Efficiency in Direct Metal Deposition of Inconel 718 with Response Surface Methodology

“…In these equations, the actual mass ow rate of the powder added to the pool is in mg/s, P is the laser power in watts, V is the scanning speed of the laser beam in mm/s, and F is the powder injection rate or the mass ow rate exiting the nozzle in mg/s. s is These relationships are used as inputs for numerical models and simulations in the part of the mass ow rate entering the part [4,5] As can be deduced from equations 3 and 4, laser power has the greatest effect on the percentage of powder absorption by the weld pool. This has also been reported in other similar studies [25].…”

“…The mass of material deposited on the surface per unit of time (real mass ow rate) and the ratio of the mass of powder added to the surface to the mass of powder exiting the nozzle (mass e ciency) are important parameters in additive manufacturing processes especially in direct metal deposition (DMD) and selective laser sintering (SLS) which determines the production speed of the part [1]. Also, for the simulation of additive manufacturing processes, it is necessary to determine the actual mass ow rate as the input of the simulation model [2][3][4][5]. Some articles use mass-energy balance equations.…”