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Thermal Characterization of Continuous Pitch Carbon Fiber 3D-Printed using a 6-Axis Robot Arm

2022· article· en· W4312753766 on OpenAlexafffund
Sinan Olcun, Roger Kempers

Bibliographic record

Venue2022 21st IEEE Intersociety Conference on Thermal and Thermomechanical Phenomena in Electronic Systems (iTherm) · 2022
Typearticle
Languageen
FieldEngineering
TopicAdditive Manufacturing and 3D Printing Technologies
Canadian institutionsYork University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsThermal conductivityMaterials scienceComposite materialFiberExtrusionBreakageThermalNozzleThermal conductivity measurementVolume (thermodynamics)Plastics extrusionMechanical engineeringEngineering

Abstract

fetched live from OpenAlex

Initial research into the 3D-printing of continuous pitch carbon fiber composites for heat exchanger applications has showed promise however, measured thermal conductivities were significantly lower than those predicted by theoretical models due to fiber breakage during the printing process. Prior studies in which the carbon fiber was printed at 45° from the printing bed showed through thermal testing and microscopic images that the angle of the nozzle was not enough the stop the breakage. A sample at a fiber volume percentage of 10.4% was expected to have a conductivity of 83.2 W/mK, yet experimental results showed only 36.6 W/mK. At other fiber volume percentages, a similar decrease in thermal conductivity of around 55% was found. In the present study, the effect of fiber extrusion angle on the thermal conductivity of the printed rasters is investigated using a redesigned extruder and hotend mounted to a 6-axis robot arm. The thermal conductivity of individual printed rasters was characterized using a heat flow apparatus and compared to those of un-broken and specified values. Overall, it is shown that printed fiber thermal conductivity increases from 442 W/mK at a printing angle of 45° to 621 W/mK at an angle of 60°. Future directions of this process and technology are discussed.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.790
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
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 head0.209
Teacher spread0.193 · 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

Citations2
Published2022
Admission routes2
Has abstractyes

Explore more

Same venue2022 21st IEEE Intersociety Conference on Thermal and Thermomechanical Phenomena in Electronic Systems (iTherm)Same topicAdditive Manufacturing and 3D Printing TechnologiesFrench-language works237,207