Surface generation on titanium alloy through powder-mixed electric discharge machining with the focus on bioimplant applications
Bibliographic record
Abstract
Abstract The inflammation around poorly osseointegrated bioimplant is one of the root causes of its failure. Therefore, the biomedical industry constantly strives for new ways to develop bioactive surfaces in permanent implants to enhance the service life. In this regard, implant surface modification at micro/nanoscales is carried out to enrich substrate with higher engineering attributes and biocompatibility. Considering the complexities of post-processing of implants, this study evaluates the potentiality of an integrated process of implant machining and surface modification, namely, powder-mixed electric discharge machining (PMEDM). Ti6Al4V ELI implant material, as substrate, is machined under two distinct (Si, SiC) mixed additive conditions using a full factorial design of experiments. The surface quality, surface morphology, recast layer depth, surface chemistry, and work hardening have been holistically investigated. The bioactivity analysis of machined surfaces shows more porosity in the case of Si powder particles (200 to 400 nm) compared to SiC (100 to 250 nm). Furthermore, the study optimized the process parameters for minimum roughness and recast layer depth considering 5 g/L powder concentration, 5A pulse current, 50 µs pulse on time for Si, and 100 µs pulse on time for SiC. A comprehensive review of surface features based on process physical science is established, and nanoscale surface topography influencing protein absorption is analyzed.
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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) | 0.000 | 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) | 0.001 | 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".