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Record W3034222400 · doi:10.18280/rcma.300203

Orthogonal Experiment on the Surface Quality of Carbon Fiber Reinforced Plastic Cut by Abrasive Water Jet

2020· article· en· W3034222400 on OpenAlexvenueno aff
Guilin Yang, Bokai Feng

Bibliographic record

VenueRevue des composites et des matériaux avancés · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicErosion and Abrasive Machining
Canadian institutionsnot available
Fundersnot available
KeywordsMaterials scienceComposite materialWater jetAbrasiveFibre-reinforced plasticJet (fluid)FiberQuality (philosophy)Mechanical engineeringMechanicsEngineering

Abstract

fetched live from OpenAlex

The abrasive water jet (AWJ) is an immensely popular tool to machine hard-to-cut materials. Taking the surface roughness as the metric of cutting quality, this paper designs and implements an orthogonal experiment for the AWJ cutting of carbon fiber reinforced plastic (CFRP), a lightweight composite widely adopted for high-precision applications. Four factors that affect cutting quality, namely, target distance, pump pressure, nozzle traversal speed, and abradant flow rate, were selected, and divided into five levels for the orthogonal design. Since different factors differ in the value on the same level, the orthogonal design was improved by the quasi-level method. The results of the orthogonal experiment show that the nozzle traversal speed exerted the greatest effect on cutting quality, followed in turn by pump pressure, abradant flow rate, and target distance; the optimal cutting quality could be achieved at the target distance of 7mm, the abradant flow rate of 5g/s, the pump pressure of 340MPa, and the nozzle traversal speed of 200mm/min. The research results provide experimental evidence for high-quality AWJ cutting of the CFRP.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.044
Threshold uncertainty score0.999

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)0.0020.000

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 head0.066
GPT teacher head0.289
Teacher spread0.222 · 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

Citations4
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueRevue des composites et des matériaux avancésSame topicErosion and Abrasive MachiningFrench-language works237,207