Additively manufactured metallic biomaterials
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
Metal additive manufacturing (AM) has led to an evolution in the design and fabrication of hard tissue substitutes, enabling personalized implants to address each patient's specific needs. In addition, internal pore architectures integrated within additively manufactured scaffolds, have provided an opportunity to further develop and engineer functional implants for better tissue integration, and long-term durability. In this review, the latest advances in different aspects of the design and manufacturing of additively manufactured metallic biomaterials are highlighted. After introducing metal AM processes, biocompatible metals adapted for integration with AM machines are presented. Then, we elaborate on the tools and approaches undertaken for the design of porous scaffold with engineered internal architecture including, topology optimization techniques, as well as unit cell patterns based on lattice networks, and triply periodic minimal surface. Here, the new possibilities brought by the functionally gradient porous structures to meet the conflicting scaffold design requirements are thoroughly discussed. Subsequently, the design constraints and physical characteristics of the additively manufactured constructs are reviewed in terms of input parameters such as design features and AM processing parameters. We assess the proposed applications of additively manufactured implants for regeneration of different tissue types and the efforts made towards their clinical translation. Finally, we conclude the review with the emerging directions and perspectives for further development of AM in the medical industry.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| 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) | 0.003 | 0.001 |
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