Manufacturability of functionally graded porous β-Ti21S auxetic architected biomaterials produced by laser powder bed fusion: Comparison between 2D and 3D metrological characterization
Bibliographic record
Abstract
Functionally graded porous structures (FGPSs) are attracting increasing interest in the manufacture of prostheses that benefit from lower stiffness and optimized pore size for osseointegration. In this work, we explore the possibility of employing FGPSs with auxetic unit cells. Their negative Poisson’s ratio was exploited to reduce the loss of connection between prosthesis and bone usually occurring in standard implant loaded under tension and therefore undergoing lateral shrinking. In addition, to further improve osseointegration and mitigate stress shielding effects, auxetic FGPSs were fabricated in this work using a novel β-Ti21S alloy characterized by a lower Young’s modulus compared to traditional α + β Ti alloys. Specifically, two different auxetic FGPSs with aspect ratio equal to 1.5 and angle θ of 15° and 25° with a relative density (ρr ) gradient of 0.34, 0.49, 0.66 and of 0.40, 0.58, 0.75 were designed and printed by laser powder bed fusion. The 2D and 3D metrological characterization of the as-manufactured structures was compared with the design. 2D metrological characterization was carried out using scanning electron microscopy analysis, while for the 3D characterization, X-ray micro-CT imaging was used. An undersizing of the pore size and strut thickness in the as-manufactured sample was observed in both auxetic FGPSs. A maximum difference in the strut thickness of −14 and −22% was obtained in the auxetic structure with θ = 15° and 25°, respectively. On the contrary, a pore undersizing of −19% and −15% was evaluated in auxetic FGPS with θ = 15° and 25°, respectively. Compression mechanical tests allowed to determine stabilized elastic modulus of around 4 GPa for both FGPSs. Homogenization method and analytical equation were used and the comparison with experimental data highlights a good agreement of around 4% and 24% for θ = 15° and 25°, respectively.
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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 | 0.000 | 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 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".