Application of Mechanistic Force Models to Features of Arbitrary Geometry at Low Material Removal Rate
Bibliographic record
Abstract
Abstract This paper presents a workpiece discretization method to apply existing cutting force models to predict the forces generated during low material removal rate robotic machining operations of features with arbitrary geometry. Two machining operations along a straight edge are modelled using this feature discretization method are shown, a chamfer pass on a sharp corner and the removal of a trapezoidal cross section. The workpiece features are measured using a high resolution laser profile scanner to obtain the volume of the features to be removed. The identified features are discretized into rectangular sections such that the cutting force models can be applied to predict the cutting forces. A linear and an exponential mechanistic model which relate tool immersion and feed rate to the cutting force are applied to the scanned workpiece features. The linear and nonlinear models show good agreement with the measured data, with the exception that the linear model occasionally over predicts the forces depending on the radial depth of cut.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".