Optimal selection of protective coatings for stainless steel-titanium alloy fasteners based on corrosion simulation
Bibliographic record
Abstract
The Bearing Quick Release Latches (BQRL) are subjected to rainfall and other influences, which lead to galvanic corrosion due to the difference in free corrosion potential between the materials of each component. In order to improve the BQRL in the marine atmospheric environment, their surfaces are treated with a protective coating, therefore, the choice of coating is an important issue. In order to select the optimum coating, this paper develops a numerical model for the corrosion of the BQRL in the marine atmospheric environment based on the physical field of the secondary current distribution in COMSOL Multiphysics®. The electrochemical parameters of the different protective coatings are measured by electrochemical experiments and imported into COMSOL Multiphysics®. By comparing the anode current density and corrosion depth of various models, the best and worst corrosion protection solutions were identified. The current density difference between them is two orders of magnitude. The reasons for the large differences in corrosion rates are analyzed and compared with the results of experiments. This paper hopes to provide a new idea for optimal coating selection through COMSOL Multiphysics®, saving time and economic costs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".