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Record W4409018570 · doi:10.4271/2025-01-8223

A New Methodology to Characterize the Influence of Paint Baking and Pre-Straining on the Tensile Properties of Third Generation Advanced High Strength Steels

2025· article· en· W4409018570 on OpenAlexaff
Rhys Northcote, Advaith Narayanan, Cameron Tolton, Jonathan Smith, Eric C. McCarty, Cliff Butcher

Bibliographic record

VenueSAE technical papers on CD-ROM/SAE technical paper series · 2025
Typearticle
Languageen
FieldEngineering
TopicMetal Forming Simulation Techniques
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsMaterials scienceUltimate tensile strengthMetallurgyComposite material

Abstract

fetched live from OpenAlex

<div class="section abstract"><div class="htmlview paragraph">The current ASTM A653 standard for determining the bake hardening index (BHI) of sheet metals can lead to premature fracture at the transition radius of the tensile specimen in high strength steel grades. In this study, a new test procedure to characterize the BHI was developed and applied to 980 and 1180 MPa third generation advanced high strength steels (3G-AHSS). The so-called KS-1B methodology involves pre-straining over-sized tensile specimens followed by the extraction of an ASTM E8 sample, paint baking and re-testing to determine the BHI. Various pre-strain levels in the range of 2 to 10% were considered to evaluate the KS-1B procedure with select comparisons with the ASTM A653 methodology for pre-strain levels of 2 and 8%. Finally, to characterize the influence of paint baking at large strain levels, sheared edge conical hole expansion tests were conducted. The tensile mechanical properties of the 3G steels after paint baking were observed to be sensitive to the pre-strain with bake hardening indices exceeding 100 MPa. However, the sheared edge formability was not significantly affected by paint baking.</div></div>

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.888
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.038
GPT teacher head0.275
Teacher spread0.238 · 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 teacher head, not a consensus.

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

Citations0
Published2025
Admission routes1
Has abstractyes

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