Mechanism and quantification of melt pool morphology evolution in single-track fabrication by laser directed energy deposition
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 it