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Feasibility Study on Grinding of Titanium Alloys with Electroplated CBN Wheels

2013· article· en· W1973997452 on OpenAlex
Zhong De Shi, Helmi Attia

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

VenueAdvanced materials research · 2013
Typearticle
Languageen
FieldEngineering
TopicAdvanced machining processes and optimization
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsGrindMaterials scienceGrindingElectroplatingTitanium alloyTitaniumMetallurgySurface roughnessGrinding wheelAlloySurface finishCutting fluidComposite materialLayer (electronics)Machining

Abstract

fetched live from OpenAlex

An experimental investigation is reported on the grinding of a titanium alloy using electroplated CBN wheels with water-based grinding fluid and wheel surface cleaning fluid applied at high pressures. This work was motivated by applying grinding fluid and wheel surface cleaning fluid both at high pressures for avoiding wheel loading, which is commonly seen in titanium alloy grinding. The objective is to explore the feasibility to grind titanium alloys with electroplated CBN wheels and high pressure wheel surface cleaning fluid for enhancing material removal rates. Straight surface grinding experiments were conducted on titanium alloy blocks in both shallow depth of cut and creep-feed modes. Grinding power, forces, and surface roughness were measured. Specific material removal rates of 8 mm 2 /s in shallow cut mode and 3 mm 2 /s at a depth of cut as high as 3 mm in creep-feed mode were achieved without burning and smearing of ground surfaces. It was showed that it is feasible to grind titanium alloys with electroplated CBN wheels at enhanced removal rates by applying grinding and wheel cleaning fluid at high pressures.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.100
Threshold uncertainty score0.550

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 head0.038
GPT teacher head0.337
Teacher spread0.299 · 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