Lattice Structures and Functionally Graded Materials Applications in Additive Manufacturing of Orthopedic Implants: A Review
Why this work is in the frame
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Bibliographic record
Abstract
A major advantage of additive manufacturing (AM) technologies is the ability to print customized products, which makes these technologies well suited for the orthopedic implants industry. Another advantage is the design freedom provided by AM technologies to enhance the performance of orthopedic implants. This paper presents a state-of-the-art overview of the use of AM technologies to produce orthopedic implants from lattice structures and functionally graded materials. It discusses how both techniques can improve the implants’ performance significantly, from a mechanical and biological point of view. The characterization of lattice structures and the most recent finite element analysis models are explored. Additionally, recent case studies that use functionally graded materials in biomedical implants are surveyed. Finally, this paper reviews the challenges faced by these two applications and suggests future research directions required to improve their use in orthopedic implants.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 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 | 0.001 |
| 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