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Record W3111830425 · doi:10.1122/8.0000052

Non-Newtonian modeling of contact pressure in fused filament fabrication

2020· article· en· W3111830425 on OpenAlexaff
Sun Kyoung Kim, David O. Kazmer, Austin R. Colon, Timothy J. Coogan, Amy M. Peterson

Bibliographic record

VenueJournal of Rheology · 2020
Typearticle
Languageen
FieldEngineering
TopicAdditive Manufacturing and 3D Printing Technologies
Canadian institutionsSaint-Gobain (Canada)
FundersNational Research Foundation of KoreaDirectorate for EngineeringNational Science Foundation
KeywordsMaterials scienceNozzleBody orificeMechanicsFlow (mathematics)Orifice plateFabricationDragRheologyNewtonian fluidVolumetric flow rateComposite materialThermodynamicsMechanical engineering

Abstract

fetched live from OpenAlex

The nozzle pressure was monitored in a fused filament fabrication process for the printing of high impact polystyrene. The contact pressure, defined as the pressure applied by the newly deposited layer onto the previous layer, is experimentally calculated as the difference between the pressure during printing and open discharge at the same volumetric flow rates. An analytical method for estimating the contact pressure, assuming one-dimensional steady isothermal flow, is derived for the Newtonian, power-law, and Cross model dependence of shear rates. A design of experiments was performed to characterize the contact pressure as a function of the road width, road height, and print speed. Statistical analysis of the results suggests that the contribution of the pressure driven flow is about twice that of the drag flow in determining contact pressure, which together describe about 60% of the variation in the observed contact pressure behavior. Modeling of the elastic and normal stresses at the nozzle orifice explains an additional 30% of the observed behavior, indicating that careful rheological modeling is required to successfully predict contact pressure.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.017
GPT teacher head0.220
Teacher spread0.203 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations32
Published2020
Admission routes1
Has abstractyes

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