The Master Sinter Curve and Its Application to Binder Jetting Additive Manufacturing
Bibliographic record
Abstract
Abstract The master sinter curve (MSC) is an empirical model used to predict the density of a part after being sintered. The model is typically applied to components that undergo isotropic shrinkage. Parts manufactured using binder jetting additive manufacturing (BJAM) are known to have nonuniform powder systems and high levels of anisotropy. This work explores the application of the master sinter curve to components made by BJAM. Cylindrical samples were manufactured with the long axis parallel (vertical), perpendicular (horizontal), and 45 deg to the printing direction. A bimodal blend of titanium powder (0–45 µm and 106–150 µm) was used to make samples with consistent green densities (ranging from 47.2% to 52.3%) between the different orientations. Samples were then sintered at heating rates of 1, 3, and 5 °C/min to a maximum of 1400 °C. After sintering, the samples showed significant variation between the different orientations, with vertical samples on average 7.6 ± 2.98% and 4.7 ± 1.20% denser than the horizontal and the 45 deg samples, respectively. The calculated apparent activation energies for sintering were within the same range for all orientations, 200–260 kJ/mol for vertical and 45 deg, and 140–260 kJ/mol for horizontal samples. Validation sinter runs showed that the density prediction errors of the master sinter curves were between 0.9% and 4.3%. This work shows that the master sinter curve can be applied to predict the sintered density of components manufactured by binder jetting additive manufacturing.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".