Mitigating Corrosion in Mg Sheet in Conjunction with a Sheet-Joining Method that Satisfies Structural Requirements within Sub-assemblies
Bibliographic record
Abstract
This work was undertaken as a LightMAT project funded by the DOE-Vehicles Technology Office. The goal of this work was to develop corrosion protection strategies that simultaneously mitigate corrosion and achieve Class-A surface finish for Mg components in automotive applications. While automotive metals such as steel and aluminum are protected against corrosion through a variety of coating schemes/packages, the efficacy of these existing coating schemes for Mg and Mg- joints is not clear and needs to be determined. Therefore, five commercially available coating schemes and two joining techniques (riveting and Arplas resistance spot welding) were evaluated. The corresponding individual Mg sheet coupons or Mg/Mg joint test coupons were provided by Magna that were then corrosion tested at PNNL using ASTM B117 procedure. The microstructures and mechanical properties of the coupons were analyzed to determine the effectiveness of the joining and corrosion mitigation strategies. Of the coating schemes evaluated, Henkel Bonderite MgC 2.0 pre-treatment + E-coat showed the best corrosion protection and surface finish for individual Mg coupons and Arplas resistance spot welded coupons. However, the strength of Mg/Mg welded joint was reduced after corrosion testing due to some corrosion at the weld nugget. Mg/Mg rivet joints in conjunction with Chemetall oxisilan pre-treatment + polyurethane coating showed good corrosion resistance and some discoloration on the surface finish. Coating schemes comprising pre-treatment with Alodine 5200 + E-coat or Bonderite 1455 + polyurethane coating, in conjunction with Al rivet joints, showed significant corrosion and extensive discoloration of the surface. We anticipate that the results from this work will provide useful guidance to the automotive industry in selecting the appropriate combinations of corrosion protection coatings and joining techniques to fabricate light-weight Mg-based automotive components.
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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 | 0.000 |
| 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".