Porosity-dependent mechanical properties of sintered titanium: RVE-based finite element modeling and Gibson–Ashby analysis
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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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".