MétaCan
Menu
Back to cohort

Development on Micro Precision Truing Method of ELID-Grinding Wheel (2nd Report: Application to Edge Sharpening of Large Wheel)

2005· article· en· W1965392258 on OpenAlex
Shao Hui Yin, Wei Min Lin, Yoshihiro Uehara, Shinya Morita, Hitoshi Ohmori, Muneaki Asami, Masaharu Ohmori

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

VenueKey engineering materials · 2005
Typearticle
Languageen
FieldEngineering
TopicAdvanced Surface Polishing Techniques
Canadian institutionsNexen (Canada)
Fundersnot available
KeywordsSharpeningGrindingEnhanced Data Rates for GSM EvolutionDiamondGrinding wheelGroove (engineering)Materials scienceDiamond grindingMechanical engineeringMetallurgyEngineering drawingComputer scienceEngineeringArtificial intelligence

Abstract

fetched live from OpenAlex

In V-groove ELID grinding process, to achieve optimal grinding performance and satisfactory surface quality and profile accuracy, metal bonded diamond grinding wheels need to be carefully sharpened. In this paper, we applied the proposed new micro-truing method consisting of electro-discharge truing and electrolysis-assisted mechanical truing to sharpen the edge of large grinding wheels. The minimum wheel tip radiuses of 6.3 and 8.5µm were achieved for the #4000 and #20000 grinding wheels. The truing mechanisms and sharpening performance are also discussed.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.188
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.010
GPT teacher head0.280
Teacher spread0.270 · 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