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Development on Micro Precision Truing Method of ELID-Grinding Wheel (1st Report: Principle & Fundamental Experiments)

2005· article· en· W2003515366 on OpenAlexaff
Hitoshi Ohmori, Shao Hui Yin, Wei Min Lin, Yoshihiro Uehara, Shinya Morita, Muneaki Asami, Masaharu Ohmori

Bibliographic record

VenueKey engineering materials · 2005
Typearticle
Languageen
FieldEngineering
TopicAdvanced Surface Polishing Techniques
Canadian institutionsNexen (Canada)
Fundersnot available
KeywordsGrindingDiamondMaterials scienceDiamond grindingMetallurgyMetallic bondingMechanical engineeringComputer scienceMetalEngineering drawingGrinding wheelEngineering

Abstract

fetched live from OpenAlex

Metal bonded diamond grinding wheels are widely used in the grinding process, especial in ELID grinding. However, truing is difficult owing to the high toughness of metal bond materials and high hardness of diamond abrasives. To realize high precision and high-efficiency truing, we propose a new micro-truing method consisting of electro-discharge truing and electrolysis-assisted mechanical truing in this paper. The process principle and fundamental experimental results are introduced, and the truing performance is discussed. Research results show that the proposed new method is effective for truing metal bonded diamond grinding wheels.

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 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: Empirical · Consensus signal: none
Teacher disagreement score0.382
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 head0.026
GPT teacher head0.314
Teacher spread0.287 · 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 teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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
Published2005
Admission routes1
Has abstractyes

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