Optimum corrosion performance using microstructure design and additive manufacturing process control
Bibliographic record
Abstract
Abstract Compatibility of traditional metallic alloys, particularly 316L stainless steel, with additive manufacturing (AM) processes, is essential for industrial applications. This involves manipulating process parameters to design microstructural features at various length scales, which enables the achievement of desired physical, mechanical, and chemical properties, leading to high-performance components with tailored functionalities. In this study, a hierarchical design approach, from the morphology of melt pools to subgrains, was employed, resulting in laser powder bed fusion (LPBF) 316L parts with subgrain sizes ranging from approximately 400 nm to 900 nm, confined within grains of about 40 µm to 60 µm. Findings indicated that variations in process parameters, specifically different scan strategies with a constant energy input of 60.9 J/m3, led to the production of high-density components with the smallest grains/subgrains in the continuous scan strategy. Additionally, using a combination of computational frameworks and experimental observations, we developed equations to elucidate the intrinsic relationship between energy density and grain size for LPBF-316L within the context of two optimal scanning strategies. Furthermore, the correlation between these designed microstructural features and corrosion behavior, focusing on the electrochemical properties of the components, was explored by manipulating key process parameters in LPBF. The results revealed a possible Hall–Petch relationship between corrosion rate and grain size, indicating that the microstructural features in the form of grains/subgrains influence the electrochemical behavior, resulting in a lower corrosion rate in samples with the smallest grains/subgrains.
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".