Temporal modulation of the laser emission power for microstructural manipulation in powder bed fusion
Bibliographic record
Abstract
Continuous Wave (CW) emission is typically employed in powder bed fusion by laser beam melting (PBF-LB/M) to additively manufacture components. Contemporary fiber laser sources also provide time-varying emission profiles which may be exploited to modify the microstructural characteristics of the depositions. In order to explore the wide range of possibilities enabled by temporal waveform modulation of the power, in the present research experimental data was coupled to a digital model of the PBF-LB/M process to develop a methodological framework to investigate the solidification mechanisms induced by different emission profiles. Detailed metallurgical analysis was conducted on single track deposition obtained with different emission modes corresponding to CW, Square Wave (SQW) and a Ramp Up (RUP) temporal profiles. The process was observed in-situ by means of high speed imaging to characterise the melt pool geometry whilst the microstructure was examined along both the longitudinal and transverse direction of the depositions, exposing grain texture and morphology. Microstructure could be tailored employing different emission profiles whereby band-like columnar grain growth at a 30° inclination with respect to the build direction was obtained with RUP with a strong (100) texture whereas CW emission promoted equiaxed fine grains. SQW emission showed a compromise between columnar and equiaxed grain growth avoiding significant pore formation which was on the other hand present in the case of RUP emission. A Computational Fluid Dynamics (CFD) model was validated by comparing the predicted melt region with the metallographic cross-sections, thus supporting the creation of a digital platform to explore emission profiles in the future. Moreover, the numerical model allowed the observation of the sub-surface melt pool geometry disclosing the dynamics of pore formation which could be associated to keyhole collapse due to abrupt power variations.
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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) | 0.001 | 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".