Model-Based Determination Of Process Force In Multi-Axis Milling
Bibliographic record
Abstract
Milling is a machining process in which productivity is highly dependent on the process force between the milling tool and the workpiece.Therefore, force control strategies are introduced to reduce the production time while ensuring a desired process force.In order to produce more complex geometries, the machine tool is provided with additional rotational degrees of freedom of the machine table or the milling tool, which is referred to as multi-axis milling.In a machine tool with a rotating machine table, inertial and gravitational forces disturb the measurement of the process force provided by the table dynamometer.This contribution introduces a model-based determination of the process force.Gravitational and inertial forces are calculated and subtracted from the measurement.Furthermore, the measured process force is transformed to align the measured process force with the process force determined by numerical engagement simulation.Experiments with and without a milling operation are conducted to validate the model quality.In the conducted experiments, the process force determined in a milling operation with a rotated machine table has a relative error of less than 13.3 % in x-and y-direction compared to a milling operation without rotation and same engagement conditions.In contrast, the process force in z-direction has a relative error of up to 38.3 % due to unmodelled disturbances.For a force control strategy, mainly the determination of the process force in the x-and y-direction is relevant.Therefore, the model is sufficiently accurate in order to be tested in a milling process with a force control strategy.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".