Force and torque modelling of drilling simulation for orthopaedic surgery
Bibliographic record
Abstract
The advent of haptic simulation systems for orthopaedic surgery procedures has provided surgeons with an excellent tool for training and preoperative planning purposes. This is especially true for procedures involving the drilling of bone, which require a great amount of adroitness and experience due to difficulties arising from vibration and drill bit breakage. One of the potential difficulties with the drilling of bone is the lack of consistent material evacuation from the drill's flutes as the material tends to clog. This clogging leads to significant increases in force and torque experienced by the surgeon. Clogging was observed for feed rates greater than 0.5 mm/s and spindle speeds less than 2500 rpm. The drilling simulation systems that have been created to date do not address the issue of drill flute clogging. This paper presents force and torque prediction models that account for this phenomenon. The two coefficients of friction required by these models were determined via a set of calibration experiments. The accuracy of both models was evaluated by an additional set of validation experiments resulting in average R² regression correlation values of 0.9546 and 0.9209 for the force and torque prediction models, respectively. The resulting models can be adopted by haptic simulation systems to provide a more realistic tactile output.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".