Optimal Calibration of Parallel Kinematic Machines
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, a new method for optimal calibration of parallel kinematic machines (PKMs) is presented. The basis of the methodology is to exploit the least error sensitive regions in the workspace to yield optimal calibration. To do so, an error model is developed that takes into consideration all the geometric errors due to imprecision in manufacturing and assembly. Based on this error model, it is shown that the error mapping from the geometric errors to the pose error of the PKM depends on the Jacobian inverse. The Jacobian inverse would introduce spurious errors that would affect the calibration results, if used without proper care. Hence, areas in the workspace with smaller condition numbers are selected for calibration. Simulations and experiments are presented to show the effectiveness of the proposed method. Calibration software based on the proposed method has been embedded in the tripod developed at the National Research Council of Canada’s Integrated Manufacturing Technologies Institute.
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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 | 0.000 | 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 | 0.000 |
| 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