Comparative assessment of gas and water atomized powders for additive manufacturing of 316 L stainless steel: Microstructure, mechanical properties, and corrosion resistance
Bibliographic record
Abstract
In this study, the microstructure, and mechanical properties of 316 L stainless steel (SS316L) produced by laser powder bed fusion (LPBF) using water-atomized (WA) and gas-atomized (GA) powders were compared. The results showed that the use of WA powder, with a finer average particle size and better spreadability, led to significantly higher values of tensile strength (UTS), yield strength (YS), elongation (El%), and toughness in the WA samples (728 MPa, 580 MPa, 31.8%, and 215 J/m 3 , respectively) compared to the GA sample (602 MPa, 503 MPa, 25.2%, 145 J/m 3 , respectively). The WA samples also exhibited a non-uniform hardness distribution and superior work-hardening rate due to the presence of multiple inclusions that tightly bound to the matrix and created stress fields, increasing the required energy for dislocation motion . The higher solidification rate of melt pools in the WA sample left more intensive residual stress with distorted grains, exhibiting a higher grain orientation spread (GOS). Additionally, a multitude of geometrically necessary dislocations (GNDs) formed around the boundaries of elongated grains with tilted boundaries to maintain lattice continuity, resulting in a higher kernel average misorientation (KAM) and congestion of low-angle grain boundaries (LAGBs), particularly in the WA sample. XRD patterns confirmed the higher lattice distortion in the WA sample, and the smaller cellular structures observed in SEM images were consistent with the higher dislocation density observed in the WA specimens. Finally, the WA sample exhibited lower surface roughness and rather higher resistance to corrosive media containing chlorides.
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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) | 0.000 | 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 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".