Characterizations of additive manufactured porous titanium implants
Bibliographic record
Abstract
This article describes physical, chemical, and mechanical characterizations of porous titanium implants made by an additive manufacturing method to gain insight into the correlation of process parameters and final physical properties of implants used in orthopedics. For the manufacturing chain, the powder metallurgy technology was combined with the additive manufacturing to fabricate the porous structure from the pure tanium powder. A 3D printing machine was employed in this study to produce porous bar samples. A number of physical parameters such as titanium powder size, polyvinyl alcohol (PVA) amount, sintering temperature and time were investigated to control the mechanical properties and porosity of the structures. The produced samples were characterized through porosity and shrinkage measurements, mechanical compression test and scanning electron microscopy (SEM). The results showed a level of porosity in the samples in the range of 31-43%, which is within the range of the porosity of the cancelluous bone and approaches the range of the porosity of the cortical bone. The results of the mechanical test showed that the compressive strength is in the wide range of 56-509 MPa implying the effect of the process parameters on the mechanical strengths. This technique of manufacturing of Ti porous structures demonstrated a low level of shrinkage with the shrinkage percentage ranging from 1.5 to 5%.
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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 | 0.001 | 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 | 0.000 |
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".