A novel additive manufacturing-based technique for developing bio-structures with conformal channels and encapsulated voids
Bibliographic record
Abstract
A novel additive manufacturing-based technique for developing bio-structures with conformal channels and encapsulated voids. The network of macro-channels in the structure of cortical bone is crucial, particularly for nutrition. Therefore, a successful bone implant should simulate the real bone architecture by including such channels in its structure. The introduction of additive manufacturing (AM) techniques in the orthopedic implant industry brings the potential of producing customized bone implants that mimic the structure of real bone. However, the depowdering issue in the powder bed AM technique has hindered the creation of macro-sized channels in bone implants composed of biocompatible materials. In this study, we introduce a new method for manufacturing implants which is composed of titanium and includes networks of channels. This technique is primarily based on printing individual components of a sliced structure, followed by depowdering and assembling the components before the sintering process. This new technique has the potential to control the internal features of 3D printed structures. A set of comparative physical and mechanical tests were conducted to characterize the resulting structures. Experimental characterization results showed that the shear strength of the sample that was made by the new technique was reduced by 24%–30%, where the porosity was slightly lower (~2%) than that of a comparable control sample. However, the new technique had no effect on the compressive strength of the structure.
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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 | 0.001 |
| 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 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".