Theory and simulation for the identification of the link geometric errors for a five-axis machine tool using a telescoping magnetic ball-bar
Why this work is in the frame
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Bibliographic record
Abstract
The position invariant geometric inaccuracies of a machine tool are the first to influence the quality of machined parts. A systematic approach is presented to identify some of these errors on a five-axis machine tool. The methodology is applied to the link error parameters such as joint misalignments, angular offset and rotary axis separation distance. A method based on the mathematical analysis of singularities of linear systems is used to assist in selecting a minimal but sufficient set of link error parameters for the calibration of a machine tool. A number of criteria are proposed in order to verify that the identified parameters accurately predict the positioning errors of the true machine. Finally, the numerical effectiveness of this method is shown through simulations.
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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 | 0.003 | 0.003 |
| 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 | 0.000 |
| 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 it