Optimization of the printability envelope of low-viscosity powder-binder feedstocks used in material extrusion 3D printing
Bibliographic record
Abstract
Purpose The purpose of this study is to determine the material extrusion (MEX) printability envelope of a new kind of low-viscosity powder-binder feedstocks using rheological properties. Design/methodology/approach Formulation of 13 feedstocks (variation of solid loading 60–67 Vol.% and thickening agent proportion 3–15 Vol.%) that were characterized and printed at different temperatures. Findings Three rheological models were successfully used to define the viscosity envelope, producing stable and defect-free printing. At a shear deformation rate experienced by the feedstock in the nozzle ranging from 100 to 300 s–1, it was confirmed that metal injection molding (MIM) feedstocks exhibiting a low viscosity between 100 and 150 Pa s could be printed using an extrusion temperature as low as 85 °C. Practical implications MEX can be used in synergy with MIM to accelerate mold development for a new injected part or simply as a replacement for MIM when the cost of the mold becomes too high for very small production volumes. Originality/value Correlation between the rheological properties of this new generation of low-viscosity feedstocks and MEX printability has been demonstrated for the first time.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".