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Friction Characterization of Al-Si Coated Ultra-High Strength Steel under Hot Stamping Conditions

2021· article· en· W3174542980 on OpenAlexafffund
Rengui He, Sante DiCecco, Ryan George, Michael J. Worswick, C. I. CHIRIAC, George Luckey, Jimi Tjong, Cangji Shi, J. C. Boettger

Bibliographic record

VenueIOP Conference Series Materials Science and Engineering · 2021
Typearticle
Languageen
FieldMaterials Science
TopicMetal Alloys Wear and Properties
Canadian institutionsFord Motor Company (Canada)University of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaOntario Advanced Manufacturing ConsortiumOntario Centres of Excellence
KeywordsMaterials scienceHot stampingSurface roughnessComposite materialDie (integrated circuit)Quenching (fluorescence)Surface finishFriction coefficientTool steelStampingGallingMetallurgy

Abstract

fetched live from OpenAlex

Abstract Friction characterization of a hot stamped 1,800 MPa Al-Si coated ultra-high-strength steel (UHSS) is considered. Quantification of the coefficient of friction during a hot stamping process is essential for an accurate constitutive finite element model. The current work considers the twist compression test (TCT), in which an annular cup rotates against the surface of a fixed specimen using heated tooling. The measured sliding force and normal force determine the coefficient of friction. The test conditions used in this research simulate thermo-mechanical histories operative during hot stamping. Specimens are austenitized in a chamber furnace at a nominal temperature of 930 °C and a hold time of 5 minutes. They are then transferred to the TCT apparatus in which frictional sliding and die quenching occur simultaneously. The tooling components are uncoated and heated to 80 °C. Contact pressure of up to 30 MPa was considered between the tooling and specimen and a sliding speed of up to 38 mm/s. An increase in contact pressure caused the coefficient of friction to increase during dynamic friction. On the other hand, changes in sliding speed did not have a significant impact on the coefficient of friction. Likewise, repeated tests on the same tooling surface show that the coefficient of friction remains consistent during tool wear. Surface roughness of the tooling only increased after the first test and then remains stable during subsequent 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 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)0.000
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.860

Codex and Gemma teacher scores by category

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

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 head0.021
GPT teacher head0.220
Teacher spread0.199 · 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.

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

Citations0
Published2021
Admission routes2
Has abstractyes

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Same venueIOP Conference Series Materials Science and EngineeringSame topicMetal Alloys Wear and PropertiesFrench-language works237,207