Cosmetic Corrosion of Aluminum Closure Panels: Lab Testing vs Field Performance
Bibliographic record
Abstract
The correlation of lab test results with field performance for painted steel and galvanized steel automotive closure panels is now well established after many years. Although aluminum closure panels have been used on certain vehicles for many years, it has only been in more recent times that their usage has increased to a point where the issue of correlating lab and field corrosion data has become more essential. Many tests in the automotive industry were developed specifically for steel and their applicability to aluminum closures is uncertain. On the other hand, many of the standard corrosion tests used on aluminum were designed for aluminum applications other than automotive, e.g., architectural or packaging, so that the test environment and product requirements were very different. In this paper, a number of standard corrosion tests, including filiform, salt spray and cycling environment, were carried out on AA6111 and AA6016 closure sheet materials for comparison with field data. Analysis of the mode of corrosion failure was made as well as comparison of the extent of corrosion. The reasons for the variations in failure modes and extent of corrosion are discussed.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.003 | 0.001 |
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".