Microstructural evolution in laser powder bed fusion of water-atomized high-carbon low-alloy steel: Analysis of melting mode effects
Bibliographic record
Abstract
Medium/High‑carbon steels are considered susceptible to cracking during rapid cooling in laser powder bed fusion (LPBF) applications. Also, water-atomized (WA) steel powders, containing oxides, have been associated with porosity defects during this process. In this study, LPBF was employed to process WA high‑carbon low-alloy steel powders under three operational regimes – conduction, transition, and keyhole modes. Analyses based on the scaling law of keyhole stability, melt pool dimensions, and x-ray computed tomography (XCT) suggested that processing under the transition mode closely resembled a stable keyhole, enabling the successfully printing of water-atomized powders to a density of 99.93 %. Subsequent heat treatment, hardness, and residual stress assessments presented a prominent structural softening and relief of compressive stress under the transition mode. This was attributed to the intense in-situ tempering and re-austenitization that occurred within each layer. Under this condition, with no post heat treatment, an ultimate tensile strength of 1250 MPa with >2.6 % elongation was achieved. Finally, the ball-on-disk test showed that wear performance of the printed steels was primarily governed by the tribo-oxides, with a limited influence from the microstructural variation.
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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".