In-Service Monitoring of Environmental Conditions and Corrosion Under Vehicles
Bibliographic record
Abstract
The automotive industry, for the past two decades, has witnessed significant changes in the materials used. A push for better fuel efficiency has led to an increase in multi-material assemblies [1], as well as an increased usage of aluminum alloys [2] with ever-changing compositions and microstructures to improve their mechanical properties. For economical and ecological reasons, the use of recycled materials is also increasing [3], which leads to more impurities in alloys. Finally, the ban on Cr6+ conversion coatings is accelerating the development of new conversion coating chemistries and primers [4]. These fast changes in materials and coating call for quick and reliable corrosion prediction tools before rolling out these new products on the market. Whether it be through the development of new corrosion cyclic tests, the use of FEA (Finite Element Analysis) models of galvanic corrosion, or even the use of machine learning predictive models, all of these tools can benefit from in-service monitoring data to improve their accuracy and reliability. This talk will present in detail sensors, whether they are commercial or custom-made, and instrumentation that NRC has been using for almost a decade for live monitoring of the under-vehicle environment and corrosion. Examples of in-service data recorded under Canadian city buses and insights extracted from such data will be presented. [1] Goede, M., Stehlin, M., Rafflenbeul, L. et al. Super Light Car—lightweight construction thanks to a multi-material design and function integration. Eur. Transp. Res. Rev. 1, 5–10 (2009). https://doi.org/10.1007/s12544-008-0001-2 [2] https://www.lightmetalage.com/news/industry-news/automotive/aluminum-continues-unprecedented-growth-in-automotive-applications/ [3] https://www.aluminum.org/Recycling [4] https://www.imts.com/read/article-details/Chrome-VI-Ban-Offers-Challenges-and-Opportunities/1674/type/Read/1 Figure 1
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".