Calibration of AM powders for Optimization of Recoating Applications using DEM
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Additive manufacturing (AM) has been a subject of significant attention from both industrial manufacturers and research communities. However, several challenges hinder the widespread implementation of this technology in the industry. Powder recoating is a crucial step in the AM process that involves achieving a uniformly packed bed of powder particles that are later melted by an energy source such as a laser or electron beam. One of the main challenges is calibrating contact model parameters accurately to match the flowability and spreadability of specific powder alloys. This paper proposes an automated calibration framework using surrogate model-based optimization. The study utilizes the Revolution Flow device by Mercury Scientific as the experimental reference system, and DEM simulations are performed using the Python API with the GPU DEM solver Demify® [1]. The proposed method is demonstrated using two AM powder samples, Ti64 and Inconel. The study's results indicate that particle-particle friction, rolling resistance, and van der Waals surface energy significantly affect the system responses. Furthermore, the validation results show good correspondence between the simulation using calibrated parameters and experimental data in different flow regimes. Overall, the proposed calibration framework has the potential to optimize powder recoating, improving the accuracy and effectiveness of the AM process.
