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Record W2154564025 · doi:10.4271/2008-01-1156

Development of an Improved Cosmetic Corrosion Test for Finished Aluminum Autobody Panels

2008· article· en· W2154564025 on OpenAlexaff
Francine Bovard, Janice Tardiff, Tracie Piscopink-Jafolla, Duncan McCune, G. J. Courval, Kevin A. Smith, Fred Lee, S. Ramamurthy, Ray Singleton, Florina Vartolas

Bibliographic record

VenueSAE International Journal of Materials and Manufacturing · 2008
Typearticle
Languageen
FieldEngineering
TopicIndustrial Vision Systems and Defect Detection
Canadian institutionsChrysler (Canada)Western UniversityNovelis (Canada)
Fundersnot available
KeywordsCorrosionAluminiumMaterials scienceForensic engineeringMetallurgyEngineering

Abstract

fetched live from OpenAlex

A task group within the SAE Automotive Corrosion and Protection (ACAP) Committee continues to pursue the goal of establishing a standard test method for in-laboratory cosmetic corrosion evaluations of finished aluminum auto body panels. The program is a cooperative effort with OEM, supplier, and consultant participation and is supported in part by USAMP (AMD 309) and the U.S. Department of Energy. Numerous laboratory corrosion test environments have been used to evaluate the performance of painted aluminum closure panels, but correlations between laboratory test results and in-service performance have not been established. The primary objective of this project is to identify an accelerated laboratory test method that correlates with in-service performance. In this paper the type, extent, and chemical nature of cosmetic corrosion observed in the on-vehicle exposures are compared with those from some of the commonly used laboratory tests

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.023
GPT teacher head0.248
Teacher spread0.225 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2008
Admission routes1
Has abstractyes

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Same venueSAE International Journal of Materials and ManufacturingSame topicIndustrial Vision Systems and Defect DetectionFrench-language works237,207