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Record W1041040779

Wear Analysis And Optimization On Impregnated Diamond Bits In Vibration Assisted Rotary Drilling (VARD)

2011· article· en· W1041040779 on OpenAlexaff
Alireza Abtahi, Stephen Butt, J. Mølgaard, Farid Arvani

Bibliographic record

Venue45th U.S. Rock Mechanics / Geomechanics Symposium · 2011
Typearticle
Languageen
FieldEngineering
TopicTunneling and Rock Mechanics
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsDrillingDiamondAcknowledgementAbrasion (mechanical)Wear resistancePower consumptionMaterials scienceTool wearVibrationRate of penetrationBit (key)Composite materialStructural engineeringComputer sciencePower (physics)EngineeringMachiningMetallurgyAcousticsPhysics
DOInot available

Abstract

fetched live from OpenAlex

must contain conspicuous acknowledgement of where and by whom the paper was presented. ABSTRACT: This is an investigation to find, understand, and optimize bit wear using of embedded diamond bits is being studied with and without vibration, to better understand the mechanisms of wear, the effect vibration on them, and to study relationships between drilling parameters including profile , focusing separately on the wear mechanisms for the bit matrix and the embedded diamonds. Tests the effect of different drilling conditi ons on bit matrix wear, diamond wear, and power consumption, working mainly with short runs in which small amounts of wear occurred. as uniaxial compressive strength and r elative abrasion resistance, by varying the proportions and curing of the included materials. Some preliminary results and observations are reported. critical for rate of penetration (ROP) and bit life minimum weight loss may overlap, but conditions for maximum ROP and minimum wear rate are abstract must contain conspicuous acknowledgement of where and by whom the paper was presented.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.968
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.011
GPT teacher head0.190
Teacher spread0.178 · 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 designSimulation or modeling
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

Citations6
Published2011
Admission routes1
Has abstractyes

Explore more

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