Development of an aluminum-based feedstock for material extrusion 3D printing
Bibliographic record
Abstract
Material extrusion (MEX) of metallic feedstock has received an increase of interest in the past 5 years. In this additive manufacturing process, a mixture of sacrificial polymeric binder is highly-filled with metallic powder (similarly to those developed for metal injection molding) and then printed layer-by-layer to shape green parts. The printed part is then debound and sintered to remove the binder and densify the final metallic part. The quantity and occurrence of adhesion defects and voids can be minimized or avoided by controlling the feedstock viscosity. As many aluminum alloys can be difficult to print using laser powder bed fusion (L-PBF) and the productivity is still rather limited with L-PBF, MEX process becomes an interesting alternative to fabricate complex-shape aluminum parts at high volume. However, such kind of aluminum-based powder-binder mixtures are simply absent from the market or scientific literature. This study aims to develop aluminum-based (AlSi10Mg) feedstocks used in MEX 3D printing. Rheological analysis will be used to correlate the feedstock viscosity with its printability. 3D printing will be performed using a laboratory plunger-based printer, while Archimedes density and SEM observations will be used to quantify the performances of such aluminum-based feedstocks.
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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 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".