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Predicting Transient Softening in the Sub-Critical Heat-Affected Zone of Dual-Phase and Martensitic Steel Welds

2013· article· en· W2150179027 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueISIJ International · 2013
Typearticle
Languageen
FieldEngineering
TopicMicrostructure and Mechanical Properties of Steels
Canadian institutionsUniversity of WaterlooMcMaster University
FundersUniversity of WaterlooArcelorMittal
KeywordsMaterials scienceMartensiteTemperingMetallurgyMicrostructureDual-phase steelSofteningFormabilityIsothermal processDuctility (Earth science)WeldingAusteniteComposite materialThermodynamicsCreep

Abstract

fetched live from OpenAlex

To improve vehicle fuel economy and crash worthiness the automotive industry has been redesigning parts from advanced high strength steels such as dual-phase and martensitic steels. These steels have high strengths with the higher formability characteristics when compared to lower strength conventional steels of similar ductility. These steels derive their unique properties from their complex microstructures containing ferrite and martensite. During assembly welding, the martensite within the sub-critical region of the heat-affected zone tempers, which locally reduces mechanical properties. Although this phenomenon is well studied, it has yet to be quantified. The present work proposes a technique to measure the softening kinetics of dual-phase and martensite steels using rapid isothermal tempering. The resulting model was then validated by predicting the heat-affect zone softening that occurs in laser and resistance spot welds as well as by comparing the microstructures of the rapid tempered samples to the microstructures found in the sub-critical heat-affected zone of welded samples.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.794
Threshold uncertainty score0.272

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.007
GPT teacher head0.224
Teacher spread0.217 · 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