Laser Powder Bed Fusion of Stainless Steel 316L for Rectangular Micropillar Array with High Geometrical Accuracy and Hardness
Bibliographic record
Abstract
This study investigates the manufacturability of laser powder bed fusion (LPBF) for fabricating SS316L powder into components with rectangular micropillar arrays on their surface. The rectangular micropillars with different width sizes from 200 to 800 μm were fabricated with various LPBF volumetric laser energy densities and building directions. Scanning Electron Microscope, Optical Microscope, and Vickers hardness test were used to observe the effect of micropillar sizes, the volumetric laser energy densities, and build directions on the morphologies, structural densification, geometrical accuracies, and hardness properties of the fabricated micropillars. In this work, the experimental results present that the optimum volumetric laser energy density to fabricate rectangular micropillars with a minor defect was 105 J/mm3. The micropillars fabricated with built direction on 0° and 45° planes had stable morphologies and geometrical accuracies. The fabricated micropillar had larger width and pitch, but smaller gap sizes than their computer aided design designs. The hardness property of fabricated micropillars was affected by the size of micropillars, volumetric laser energy density, and build directions during the LPBF process. In conclusion, the rectangular micropillars with width sizes of 200–800 μm were successfully fabricated on the component's surface created at 0° and 45° planes by LPBF technique using 105 J/mm3 volumetric laser energy density.
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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".