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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 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

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.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.928
Threshold uncertainty score0.848

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 head0.019
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