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Record W2948435566 · doi:10.31399/asm.hb.v18.a0006370

Fundamentals of Tribology in Metal Forming

2017· book-chapter· en· W2948435566 on OpenAlexaff
Steven R. Schmid, Jack Jeswiet

Bibliographic record

VenueASM International eBooks · 2017
Typebook-chapter
Languageen
FieldEngineering
TopicTribology and Lubrication Engineering
Canadian institutionsQueen's University
Fundersnot available
KeywordsTribologyForgingFlatteningMaterials scienceMetal workingMechanical engineeringMetal formingMetallurgyIsothermal processManufacturing engineeringEngineeringPhysics

Abstract

fetched live from OpenAlex

Abstract This article describes friction force as a function of normal force in dry forming. It focuses on metal forming operations usually classified as cold working and hot working based on metallurgical considerations. The article discusses surface flattening and roughening of workpiece asperities in metal forming. It presents advanced tribology models and results for friction in isothermal forging operations in which the tooling is maintained at a temperature close to that of the workpiece. The article provides information on heat transfer models. It discusses the effect of wear in manufacturing processes. The article concludes with information on the main categories of tool and die materials used for a variety of manufacturing application.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.024
Threshold uncertainty score0.080

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0010.002
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0240.014

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 head0.019
GPT teacher head0.241
Teacher spread0.223 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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

Citations1
Published2017
Admission routes1
Has abstractyes

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