Dimpled drill-bit to minimize thrust-force in bone drilling with <i>in-vitro</i> experimental validation
Bibliographic record
Abstract
Orthopedic surgery is a clinical procedure used to treat the damaged or diseased bones, joints, ligaments and tendons through milling, sawing, drilling and grinding operations. However, drilling through bone is the most widely used machining process that enables implant placement, fracture fixation and defect site reconstruction. In the clinical procedure, the bone screws are first guided through the ovoid holes of the compression plate and tightened through the bone using pre-drilled holes. Excessive thrust force produced during drilling into bone results in micro-cracks and bone fragmentation, which loosens the implant soon after fixation. As such, control over the thrust-force is required to avoid post-surgical complications. This study intended to minimize the thrust force produced while drilling into the bone by modifying the margins and flank faces of the widely used 3.20 mm diameter twist drill-bit. Finite element analysis coupled using the combination of the Johnson-Cook model and the Cowper-Symonds model was utilized for the drilling simulations. To authenticate the findings of the simulation with experiments, a 3.20 mm diameter twist drill-bit with dimples generated on the margins and flank faces was used. Results showed that the simulations conducted using manual and robotic-assisted bone drilling parameters were in excellent agreement with the experiments. The drill-bit modified using dimples on the margins and flank faces could effectively reduce the thrust force by a maximum of 12.31% compared with a normal drill-bit.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".