Effect of Laser Beam Profile on Thermal Transfer, Fluid Flow and Solidification Parameters during Laser-Based Directed Energy Deposition of Inconel 718

Abstract

The profile of the laser beam plays a significant role in determining the heat input on the deposition surface, further affecting the molten pool dynamics during laser-based directed energy deposition. The evolution of molten pool under two types of laser beam, super-Gaussian beam (SGB) and Gaussian beam (GB), was simulated using a three-dimensional numerical model. Two basic physical processes, the laser–powder interaction and the molten pool dynamics, were considered in the model. The deposition surface of the molten pool was calculated using the Arbitrary Lagrangian Eulerian moving mesh approach. Several dimensionless numbers were used to explain the underlying physical phenomena under different laser beams. Moreover, the solidification parameters were calculated using the thermal history at the solidification front. It is found that the peak temperature and liquid velocity in the molten pool under the SGB case were lower compared with those for the GB case. Dimensionless numbers analysis indicated that the fluid flow played a more pronounced role in heat transfer compared to conduction, especially in the GB case. The cooling rate was higher for the SGB case, indicating that the grain size could be finer compared with that for the GB case. Finally, the reliability of the numerical simulation was verified by comparing the computed and experimental clad geometry. The work provides a theoretical basis for understanding the thermal behavior and solidification characteristics under different laser input profile during directed energy deposition.

Comparative assessment of supervised machine learning algorithms for predicting geometric characteristics of laser cladded inconel 718

“…To realize the complex mapping between laser cladding process parameters and cladding layer quality, three methods are commonly used: (i) statistical analysis method, to establish the regression model between the process parameters and the response [10][11][12]; (ii) using finite element analysis methods, the established threedimensional model controls each parameter variable, simulates the experimental process of laser cladding, and predicts the desired experimental results [13][14][15][16]; (iii) application of machine learning (ML) algorithms such as Random forest regression (RFR), Support Vector Machine (SVM), Artificial Neural Network (ANN), and Deep Learning [16][17][18][19][20][21].…”

Laser Processing and Multi-Energy Field Manufacturing of High-Performance Materials

“…However, there has been little research on the laser-powder interaction and corresponding heat transport of powders under the laser intensity input with super-Gaussian distribution. Chen et al [3] proposed an improved thermal-fluid model including a laser-powder interaction model and a metal deposition model to explore the thermal-fluid transport and solidification characteristics under two types of laser beams (Gaussian and super-Gaussian) during the single-track L-DED process of Inconel 718. The deposition surface of the molten pool was calculated using the Arbitrary Lagrangian Eulerian moving mesh approach.…”