Structured porous 17-PH stainless steel layer fabrication through laser powder bed fusion
Bibliographic record
Abstract
This study aimed to fabricate thin, porous layers of 17-4 PH stainless steel with a defined porosity using laser powder bed fusion (LPBF). The central composite design (CCD) approach was utilized to examine the effect of process parameters on porosity. Three different methods were employed to measure the porosity of 17-4 PH stainless steel samples, i.e. theoretical analysis, buoyancy, and X-ray computed tomography (X-CT). A statistical quadratic regression model is generated that correlates with LPBF parameters to forecast porosity with high prediction accuracy. The maximum obtained porosity is 51.25% ± 0.33% with a laser power of 60 W, a scanning speed of 1800 mm/s, and a hatch spacing of 0.115 mm, which resulted in an average pore size of 24.8 ± 0.38 µm. Permeability was also analysed, as the volume energy density decrease ranges from 19.30 to 14.22 J/mm3, the permeability coefficient increases from 1.39 to 9.41 × 10−11 m2. In addition, it is observed that the minimum energy density to fabricate the 17-4 PH SS with the highest porosity and free of defects and fragmentation is 14.2 J/mm3.
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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".