Corrosion Behavior of Laser Powder Bed Fusion Fabricated Stainless Steel 316L
Bibliographic record
Abstract
Metal additive manufacturing techniques have been recognized for their capability of controlling the crystallographic orientations of stainless steels. However, the inherent anisotropic corrosion behavior has not been extensively studied. In this study, the corrosion properties of 316L stainless steels prepared by Laser Powder Bed Fusion (LPBF) additive manufacturing were investigated. The effects of different crystallographic textures, namely {100}, {110} and {111} on both general and pitting corrosion were characterized by several electrochemical measurements, including Electrochemical Impedance Spectroscopy (EIS), potentiodynamic polarization and Mott-Schottky analysis. The results were also compared to the polycrystalline and wrought 316L counterparts. It was found that the LPBF-{111} sample offered the highest general corrosion resistance, followed by the LPBF-{100}, LPBF-polycrystalline and LPBF-{110} samples (Figure 1). The origin of this trend was related to the atomic surface density. The LPBF-{111} surface exhibited a stronger atomic bonding than that of LPBF-{100} and LPBF-{110} samples, resulting in a higher corrosion activation energy and thus a higher general corrosion resistance. All the LPBF samples also offered a significantly higher pitting corrosion resistance (Figure 2), which was attributed to the lower concentration of oxygen vacancies (donor levels) in the passive film that serve as pits nucleation sites, as observed by the Mott-Schottky analysis (Figure 3). Figure 1
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".