Viscosity of Mono‐ and Polydisperse Mixtures of Photopolymer and Rigid Spheres for Manufacturing of Engineered Composite Materials Using Vat Photopolymerization
Bibliographic record
Abstract
Vat photopolymerization (VP) additive manufacturing involves selectively curing low‐viscosity photopolymers via exposure to ultraviolet light in a layer‐wise fashion. Dispersing filler materials in the photopolymer enables tailored end‐use properties, but also increases the viscosity and the timescale associated with interparticle network structural recovery postshear. These rheological properties influence self‐leveling and recoating of the liquid photopolymer mixture during VP. Herein, viscosity of photopolymer and rigid spherical glass microparticles (filler) is experimentally determined as a function of filler fraction, filler size distribution (mono‐ and polydisperse), shear rate, and temperature, which are important VP process parameters. Employing existing viscosity models for mono‐ and polydisperse polymer mixtures demonstrates that particle–particle interactions and the formation of nonspherical clusters of particles strongly affect the viscosity of both monodisperse and polydisperse mixtures with particle volume fractions > 0.05 due to agglomeration/deagglomeration of clusters at elevated shear rates. Consequently, unmodified viscosity models, which assume uniformly dispersed, rigid, spherical particles, are applicable only for mixtures with particle volume fractions < 0.05. It is shown that modifying model parameters such as the fluidity limit and intrinsic viscosity, which explicitly account for nonspherical clusters of particles, improves agreement between viscosity models and experiments, in particular when using a fractal approach.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".