In-Service Corrosion Performance of Automotive AA7075 Sheet Alloy
Bibliographic record
Abstract
Aluminum alloys offer higher specific strength than advanced high strength steels, making them preferred material choice for automotive light weighting. Among them, AA7075 sheet alloy offers significantly higher strength than 5xxx and 6xxx alloys and has been developed to provide high in-service strength for automotive structural applications. However, there is a need to understand the stress corrosion cracking (SCC) susceptibility of AA7075 under “in-service” conditions for the automotive market. Currently there are no commonly accepted corrosion testing specifications or evaluation criteria for AA7075 sheets among material suppliers and automotive original equipment manufacturers. Furthermore, there is a lack of in-service data to validate the accelerated laboratory exposure testing. In this study, in-service SCC performance of the AA7075 test coupons was evaluated by road exposure for a two-year period under harsh Canadian winters. The SCC specimens were loaded using four-point bending frames, and were investigated in various tempers and surface conditions (bare vs. e-coated). Overall, the road exposure results were consistent with the lab accelerated testing such as slow strain rate tensile testing in specific environments. Coating provided sufficient corrosion protection such that the stressed coupons survived after 2-year road exposure without reduction in strength. For bare metals, overaged T73 and in-service (T6+paint bake) tempers outperformed T6 temper. Localized corrosion of AA7075 sheets with various tempers was also studied using potentiodynamic polarization technique. Acknowledgements: Dr. Danick Gallant, Aluminum Technology Center at National Research Council Canada; Kennesaw lab support in Novelis Global Research and Technology Center.
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 | 0.000 |
| 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) | 0.001 | 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".