Environmentally conscious biomedical implant manufacturing method
Bibliographic record
Abstract
The significant advantage of using magnesium as a surgical implant is its ability to biodegrade in situ, eliminating the requirement for implant removal surgery. However, investigating biomedical implant fabrication processes required the study analyses from the perspective of the process plan and environmental impact for better sustainable solutions. In the present study, magnesium and hydroxyapatite-based composite material have been selected for fabricating the bio-medical implant. The real-time data has been observed in the production processes. The environmental impacts of the produced bio-implant material are compared with those of the bio-implant produced using friction-stir processing and laser-based powder metallurgy. It is observed from the analysis that conventional methods can be utilized for making bio-medical implants cost-effectively after careful modifications in the process sequence and parameters. The environmental impact analysis provided a detailed visualization of major hotspots and supported effective decision-making toward improving the production process. At the same time, the comparative analysis provides a clear picture of the hotspots in the bio-implant production processes. The study compared the environmental impact of two bio-implant production processes and provided valuable insights to reduce overall cost and improve environmental impact across the supply chain. A bio-implant prototype has been made with a cheaper alternative to additive manufacturing.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".