Optimum corrosion performance using microstructure design and additive manufacturing process control
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Compatibility of traditional metallic alloys, particularly 316 L stainless steel, with additive manufacturing (AM) processes, is essential for industrial applications. This involves manipulating process parameters to design microstructures at various length scales, achieving desired properties for high-performance components. In this study, a hierarchical design approach was used for LPBF 316 L parts, achieving cell sizes of 400 to 900 nm confined within grains of 40 to 60 μm. Findings showed that varying scan strategies with constant energy input produced high-density components, with the smallest grain and cell size achieved in the continuous scan strategy. In addition, equations were developed to connect energy density with grain size for LPBF-316L, highlighting optimal scanning strategies. Furthermore, the correlation between microstructural features and corrosion behavior, focusing on electrochemical properties, was explored by adjusting key LPBF process parameters. The results suggested a Hall-Petch relationship between grain size and corrosion rate, indicating that smaller grains and cells reduce corrosion rates by affecting electrochemical behavior.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 it