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Record W4392372901 · doi:10.1177/09544062241235198

Working mechanism and experimental study of split bit

2024· article· en· W4392372901 on OpenAlexaff
Jialin Tian, Yuhang Wu, Zhe Deng, Liming Dai

Bibliographic record

VenueProceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science · 2024
Typearticle
Languageen
FieldEngineering
TopicAdvanced Surface Polishing Techniques
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsBit (key)Mechanism (biology)Computer scienceArithmeticMathematicsEpistemologyComputer networkPhilosophy

Abstract

fetched live from OpenAlex

As a component directly acting on the rock in the drilling process, the low efficiency of rock breaking will occur when the bit is drilled. In this paper, a new type of split drill is designed in which the cutting ring can move relatively independently, as well as the kinematic analysis model of the cutting tooth is established. The displacement, velocity and acceleration values of the specific points on the cutting tooth ring of the bit are obtained, which verifies that the small cutting ring at the center of the split cone bit can improve the rock breaking efficiency at the center of the bit. Through the comparison experiments of different types of bits and split bits, the results show that the mechanical drilling rate of split single-tooth bits and triple-tooth bits increased by 11.7% and 12.4%, respectively, in Beipei limestone conditions. Meanwhile, the speed of those two increased by 8.9% and 11.4%, respectively, in Wusheng sandstone conditions, which effectively improved the rock-breaking performance. Combined with the results of bottom hole model, the characteristics of impact, invasion and cutting in the process of tooth rock breaking can be obtained, which effectively improves the rock breaking performance and verifies the rationality of bit design and the correctness of theoretical analysis.

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)0.002
metaresearch head score (Gemma)0.001
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 score0.212
Threshold uncertainty score0.910

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.016
GPT teacher head0.254
Teacher spread0.238 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations0
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering ScienceSame topicAdvanced Surface Polishing TechniquesFrench-language works237,207