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Record W2169817993 · doi:10.1177/0954406211400670

Determination of rotary cutting depth on steel pipes with the abrasive water jet technique

2011· article· en· W2169817993 on OpenAlexaff
H Li, R Wang, Daoyong Yang, Wei Zhou, L Li

Bibliographic record

VenueProceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicErosion and Abrasive Machining
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsNozzleAbrasiveMachiningRotational speedRotation (mathematics)Materials scienceMechanical engineeringJet (fluid)MechanicsMetallurgyEngineeringMathematicsGeometryPhysics

Abstract

fetched live from OpenAlex

Abrasive water jet (AWJ) cutting is a green machining and processing technology that has found extensive applications. In this study, a well-designed cutting tool with multiple AWJ nozzles has been developed to determine the rotary multi-pass cutting depth on steel pipes that are used in petroleum industries. Experimentally, the multi-pass AWJ cutting depth is found to increase with pump pressure, nozzle diameter, and number of nozzles, but decrease with standoff distance. Also, the multi-pass cutting depth initially increases with rotation speed of the AWJ cutting tool or volumetric concentration of abrasive, and then begins to decline when rotation speed or volumetric concentration reaches a certain value. Mathematically, an empirical model is formulated to determine the rotary cutting depth initiated by multiple cutting passes based on the energy conservation theory. There exists a good agreement between the experimentally measured and theoretically calculated cutting depths with a percentage average absolute deviation of 7.0 per cent.

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 score0.094
Threshold uncertainty score0.411

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.013
GPT teacher head0.213
Teacher spread0.200 · 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

Citations10
Published2011
Admission routes1
Has abstractyes

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