Experimental analysis of the process parameters affecting bone burring operations
Bibliographic record
Abstract
The experimental quantification of the process parameters associated with bone burring represents a desirable outcome both from the perspective of an optimized surgical procedure as well as that of a future implementation into the design of closed-loop controllers used in robot-assisted bone removal operations. Along these lines, the present study presents an experimental investigation of the effects that tool type, rotational speed of the tool, depth of cut, feed rate, cutting track overlap, and tool angle (to a total of 864 total unique combinations) have on bone temperature, tool vibration, and cutting forces associated with superficial bone removal operations. The experimental apparatus developed for this purpose allowed a concurrent measurement of bone temperature, tool vibration, and cutting forces as a function of various process parameter combinations. A fully balanced experimental design involving burring trials performed on a sawbone analog was carried out to establish process trends and subsets leading to local maximums and minimums in temperature and vibration were further investigated. Among the parameters tested, a spherical burr of 6 mm turning at 15,000 r/min and advancing at 2 mm/s with a 50% overlap between adjacent tool paths was found to yield both low temperatures at the bone/tool interface and minimal vibrations. This optimal set of parameters enables a versatile engagement between tool and bone without sacrificing the optimal process outcomes.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".