A numerical model was developed for predicting the bead geometry and microstructure in laser beam welding of 2 mm thickness Inconel 718 sheets. The experiments were carried out with a 1 kW maximum power fiber laser coupled with a galvanometric scanner. Wobble strategy was employed for sweeping 1 mm wide circular areas for creating the weld seams, and a specific tooling was manufactured for supplying protective argon gas during the welding process. The numerical model takes into account both the laser beam absorption and the melt-pool fluid movement along the bead section, resulting in a weld geometry that depends on the process input parameters, such as feed rate and laser power. The microstructure of the beads was also estimated based on the cooling rate of the material. Features such as bead upper and bottom final shapes, weld penetration, and dendritic arm spacing, were numerically and experimentally analyzed and discussed. The results given by the numerical analysis agree with the tests, making the model a robust predictive tool.
A numerical model was developed for predicting the bead geometry and microstructure in Laser Beam Welding of 2 mm thickness Inconel 718 sheets. The experiments were carried out with a 1 kW maximum power fiber laser coupled with a galvanometric scanner. Wobble strategy was employed for sweeping 1 mm wide circular areas for creating the weld seams and a specific tooling was manufactured for supplying protective Argon gas during the welding process. The numerical model takes into account both the laser beam absorption and the melt-pool fluid movement along the bead section, resulting in a weld geometry that depends on the process input parameters, such as feed rate and laser power. The microstructure of the beads was also estimated based on the cooling rate of the material. Features as bead upper and bottom final shapes, weld penetration and dendritic arm spacing were numerically and experimentally analyzed and discussed. The results given by the numerical analysis agree with the tests, making the model a robust predictive tool.
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