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Record W2037367186 · doi:10.1115/1.4023290

Characterization and Micromilling of Flow Induced Aligned Carbon Nanotube Nanocomposites

2013· article· en· W2037367186 on OpenAlexafffund
Mehdi Mahmoodi, Md. Golam Mostofa, Martin Byung‐Guk Jun, Simon S. Park

Bibliographic record

VenueJournal of Micro and Nano-Manufacturing · 2013
Typearticle
Languageen
FieldMaterials Science
TopicCarbon Nanotubes in Composites
Canadian institutionsUniversity of VictoriaUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceCarbon nanotubeComposite materialNanocompositeMachinabilityThermal conductivityPercolation thresholdPolystyrenePolymerPolymer nanocompositeMachiningElectrical resistivity and conductivity

Abstract

fetched live from OpenAlex

Carbon nanotube (CNT) based polymeric composites exhibit high strength and thermal conductivity and can be electrically conductive at a low percolation threshold. CNT nanocomposites with polystyrene (PS) thermoplastic matrix were injection-molded and high shear stress in the flow direction enabled partial alignment of the CNTs. The samples with different CNT concentrations were prepared to study the effect of CNT concentration on the cutting behavior of the samples. Characterizations of CNT polymer composites were studied to relate different characteristics of materials such as thermal conductivity and mechanical properties to micromachining. Micro-end milling was performed to understand the material removal behavior of CNT nanocomposites. It was found that CNT alignment and concentrations influenced the cutting forces. The mechanistic micromilling force model was used to predict the cutting forces. The force model has been verified with the experimental milling forces. The machinability of the CNT nanocomposites was better than that of pure polymer due to the improved thermal conductivity and mechanical characteristics.

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 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 score0.005
Threshold uncertainty score0.800

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.008
GPT teacher head0.206
Teacher spread0.199 · 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

Citations19
Published2013
Admission routes2
Has abstractyes

Explore more

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