Mechanism and quantification of melt pool morphology evolution in single-track fabrication by laser directed energy deposition
Bibliographic record
Abstract
By enabling the fabrication of complex, customized geometries, laser directed energy deposition (LDED) has emerged as a powerful technique for producing thin-wall structures widely employed in the aerospace sector. Achieving high-dimensional accuracy and geometric uniformity in these structures relies on optimizing the quality of single-layer melt tracks, which is governed by the evolution of the melt pool during deposition. Key processing parameters, including laser power ( P ), scan speed ( v ), and powder feeding rate ( f ), directly affect the static geometry and dynamic fluctuations of the melt pool. In this study, we develop a computational fluid dynamics-based simulation to investigate the longitudinal evolution of melt pool morphology during the formation of SS316L single tracks, focusing on laser activation, steady-state, and deactivation stages. The melt pool expands and tilts during laser activation due to thermal imbalance, exhibits surface fluctuations in a flat → bulge → wave pattern during the steady state, and contracts centripetally as solidification progresses during deactivation. An in situ high-speed infrared imaging system is integrated into the LDED setup for real-time monitoring of the melt pool. High-throughput experiments spanning 360 P - v - f combinations are conducted and automatically analyzed to quantify static features and dynamic fluctuations of the melt pool. Based on these results, a quality metric for melt tracks is proposed to identify optimal processing windows, which are experimentally verified through the fabrication of thin-wall samples with improved dimensional fidelity and geometric uniformity. The findings of this work provide critical insights into melt pool dynamics and offer a systematic approach for the optimization of processing parameters in LDED.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".