Bibliographic record
Abstract
An in-line rheometer and data acquisition system are used to monitor the melt pressure, melt temperature, and environmental temperatures while producing parts via fused filament fabrication (FFF). Melt pressures are observed to increase when printing parts with small layer heights, which is attributed to the confined space created between the nozzle and the previous layer (i.e., an exit pressure). These exit pressures (referred to as contact pressure) and the resulting interlayer contact areas are analyzed for 2863 layers created at 21 different processing conditions. The measured contact pressure was found to directly influence the shape of the layers and the resulting interlayer contact. An intimate contact model based on contact pressure is combined with a wetting model to accurately predict the interlayer contact of FFF parts. This pressure-driven intimate contact model for FFF shows strong agreement with the observed interlayer contact. No theoretical model has previously existed for predicting interlayer contact, so this research provides a critical component for developing a comprehensive part strength model. Both the measurements and proposed model are sufficiently simple and accurate for real-time analysis of FFF quality, so the described in-line sensors provide valuable quality insights and are recommended for future researchers, printer manufacturers, and end-users.
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 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.244 | 0.549 |
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".