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Record W2047662146 · doi:10.1243/095440506x77580

Methods and Devices Used to Measure Friction in Rolling

2006· article· en· W2047662146 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

VenueProceedings of the Institution of Mechanical Engineers Part B Journal of Engineering Manufacture · 2006
Typearticle
Languageen
FieldEngineering
TopicMetallurgy and Material Forming
Canadian institutionsQueen's University
Fundersnot available
KeywordsMeasure (data warehouse)TribologyInterface (matter)Mechanical engineeringMetal formingComputer scienceMaterials scienceEngineeringComposite materialData mining

Abstract

fetched live from OpenAlex

Friction at the workpiece-die boundary, in both bulk forming and sheet forming is, arguably, the single most important physical parameter influencing the processing of metals; yet it remains the least understood. Hence there is a need for basic research into metal-die interface mechanisms. To gain a good understanding of the mechanisms at the interface and to be able to verify the friction and tribology models that exist, friction sensors are needed. Designing sensors to measure frictional stress in metal working has been pursued by many researchers. This paper surveys methods that have been used to measure friction in rolling in the past and discusses some of the recent sensor designs that can now be used to measure friction both in production situations and for research purposes.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.571
Threshold uncertainty score0.566

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 head0.014
GPT teacher head0.233
Teacher spread0.219 · 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