Influence of heat treatment on the mechanical performance of Ti21S octet truss lattice structure fabricated by laser powder bed fusion
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Additive manufacturing allows the production of complex and custom designs including lattice structures—porous metallic structures with designed porosity and tailored mechanical properties. The bulk material has a key influence on the eventual properties of the porous lattice structure material. Among metallic biomaterials, beta-titanium alloys are gaining increasing interested because of their low Young’s modulus. In this work, the heat treatment of beta-Ti21S alloy is investigated in the context of octet truss lattice structures. The intention is to improve the performance of these structures for their reliable use in biomedical applications such as for bone implants. The study makes use of laser powder bed fusion of representative samples, uses microCT for physical characterization of manufacturing quality, while quasi-static and fatigue testing is performed to evaluate the performance of these lattice structures. The results indicate that the heat treatment significantly improves the fatigue properties of the lattice structures while changing the quasi-static failure mode more towards a stretch-based failure mode. These findings have practical implications for the implementation of this material and structure combination in medical implants. By enhancing the performance of the lattice structures, the study paves the way for their reliable use in biomedical applications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 it