Optimum corrosion performance using microstructure design and additive manufacturing process control
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".