Achieving high-fidelity simulation of the directed energy deposition process requires an integrated approach that captures the critical coupling between powder delivery and melt pool evolution. This paper introduces an innovative coupled computational model that bridges this gap by simultaneously resolving the powder stream dynamics and the melt pool thermo-fluid behavior. The novelty lies in its two-way interaction algorithm, where the powder particles provide mass and enthalpy sink/source terms to the melt pool, while the pool’s thermal field and surface morphology influence the particle adhesion and incorporation. Validated with synchronized experimental diagnostics, the model quantitatively links process parameters (e.g., laser power, powder feed rate) to resultant deposit qualities. Key findings include the identification of a non-linear interaction window where powder flow significantly dampens Marangoni convection, thereby altering solidification patterns. Quantitative comparisons between the simulated bead geometry and experimental metallographic cross-sections demonstrate an exceptional goodness-of-fit, confirming the model’s predictive accuracy. Consequently, the proposed model serves as a powerful virtual platform for defect prediction and precise process optimization, marking a significant step toward robust and intelligent metal additive manufacturing.
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