Binder jetting additive manufacturing of hydroxyapatite powders: Effects of adhesives on geometrical accuracy and green compressive strength
Why this work is in the frame
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Bibliographic record
Abstract
Binder jetting additive manufacturing (AM) is a promising process to print hydroxyapatite (HA) powder into bone tissue implants. However, one challenge remaining is the poor reactivity between HA powder with standard water-based ink. This study investigated different water-soluble adhesives to increase the 3D printability of HA powder. Maltodextrin and polyvinyl alcohol (PVOH) with low and high molecular weight (MW) were blended with HA from 10 to 30 wt%. Powder characterisation and evaluation of the compressive properties and geometrical accuracy of the 3D printed scaffolds were performed to identify the optimal adhesive powder. This study adopted an image registration technique to quantify the geometrical accuracy of the final 3D printed scaffold in a more comprehensive and representative way than conventionally dimensional measurement. With these approaches, a highly promising binder jetting formulation has been developed via mixing HA powder with 30 wt% PVOH (high MW). Samples manufactured from this formulation successfully achieved a geometrical accuracy greater than 85% and an excellent green compressive strength of 5.63 ± 0.27 MPa, which was 500% higher than the commercial binder jetting powder. This is the first study to demonstrate a high level of printability when using a formulation containing ≥ 70 wt% HA powder and a water-based binder in the binder jetting AM process. Using the optimal powder composition developed in this study could potentially improve the structural, mechanical, and biological performances of HA-based 3D scaffolds manufactured using the binder jetting AM process for bone tissue engineering applications.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 it