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Record W3140674581 · doi:10.1115/1.4049621

The Difficulties of Predicting the Coefficient of Friction in Cold Flat Rolling

2021· article· en· W3140674581 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.

Bibliographic record

VenueJournal of Tribology · 2021
Typearticle
Languageen
FieldEngineering
TopicMetallurgy and Material Forming
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsSTRIPSFriction coefficientCoefficient of frictionHardening (computing)Materials scienceAluminiumFrictional coefficientMechanicsStrain hardening exponentMetallurgyComposite materialPhysics

Abstract

fetched live from OpenAlex

Abstract The flat rolling process is initiated when the frictional forces draw the strip to be rolled into the roll gap. These forces depend on the coefficient of friction, knowledge of which is essential to understand, describe, and analyze the process. Several predictive formulae for the coefficient have been presented in the technical literature. Contradictions are observed, however, when their predictions are compared to each other. The data obtained while cold rolling aluminum and steel strips are used in the analyses. A model of the rolling process—accounting for strain hardening, frictional events, and varying speeds—is then used to determine the coefficients of friction. The use of statistical analyses is found to yield more reliable results than the use of the predictive relations.

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)0.001
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 score0.262
Threshold uncertainty score0.101

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 communication0.0000.000
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 head0.009
GPT teacher head0.211
Teacher spread0.203 · 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