Porosity-dependent mechanical properties of sintered titanium: RVE-based finite element modeling and Gibson–Ashby analysis
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Bibliographic record
Abstract
Porous sintered titanium structures are widely used in biomedical and lightweight engineering applications due to their tunable mechanical performance and favorable biocompatibility. In this study, the porosity-dependent mechanical behavior of sintered titanium is investigated using a three-dimensional representative volume element (RVE) combined with finite element modeling. The RVE is constructed from an ordered arrangement of spherical titanium particles under periodicity, and sintering-induced neck growth is represented geometrically by controlled interparticle overlap achieved through systematic reduction of the RVE size. Uniaxial displacement-controlled loading is applied to extract the homogenized stress–strain response over a range of porosity levels. The simulations demonstrate a strong sensitivity of the effective elastic modulus, yield strength, and energy absorption capacity to relative density and neck evolution. The effective mechanical properties are evaluated through homogenization and analyzed within the framework of Gibson–Ashby scaling. The normalized stiffness, strength, and absorbed energy exhibit clear power-law relationships with relative density, with scaling exponents consistent with values reported for sintered and cellular metallic materials. The results highlight the critical role of microstructural architecture and particle connectivity in governing stiffness degradation, strength reduction, and energy absorption during densification. Overall, the proposed RVE-based numerical framework provides a physically consistent and computationally efficient tool for predicting the effective mechanical response of sintered titanium as a function of porosity, offering valuable insights for the design and optimization of porous titanium components in biomedical and structural 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 0.000 |
Machine scores (provisional)
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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