The impacts of post‐processing treatments done under reactive and non‐reactive atmospheres on the densification of binder jetting parts
Bibliographic record
Abstract
Abstract At the intersection between chemical and materials engineering, binder jetting is an additive manufacturing technique that foregoes many of the limitations of the conventional metal 3D printing technique. Binder composition and thermal post‐processing influence final part quality. Herein, three different atmospheres, ranging from reactive (air) to non‐reactive (vacuum, argon), are studied to determine their effect on the quality of binder jetting printed parts after thermal treatments, namely debinding and sintering. These parts were printed using SS316L powder. Samples debound in air were negatively affected as they presented high porosities and low densities, averaging 6.80 (±0.4) g/cm 3 , whereas samples debound and sintered under vacuum demonstrated the best outcome with low porosities and high densities, averaging 7.65 (±0.1) g/cm 3 . This study highlights how oxide formation during debinding causes ferrite phase formation during sintering and how it affects atomic diffusion in the samples during both thermal treatments. This research also demonstrates the respective effects of these on the densification and porosity of parts.
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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 | 0.000 | 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".