A SELF-CALIBRATION METHOD FOR ARTICULATED ARM COORDINATE MEASURING MACHINES
Bibliographic record
Abstract
To improve the accuracy of articulated arm coordinate measuring machines (AACMM) and simplify the calibration process, an improved self-calibration method was proposed. Unlike the traditional calibration methods, which need external expensive precision instruments and elaborate setups, the proposed self-calibration method only requires a gauge to assist the data acquisition operation. By designing a movement trajectory of the AACMM, a series of joint angles can be obtained to form overdetermined equations based on the kinematic model of the AACMM. Therefore, the structural parameters of the AACMM can be obtained by solving the equations. Consequently, the calibration can be achieved by solving these equations. The coefficient matrix of the equations was further analyzed to simplify the equations, and a constructive method was presented to identify the structural parameters by solving the simplified equations with a modified simulated annealing algorithm, in which an optimized search strategy was applied to improve the robustness and efficiency. Experimental studies on an AACMM validate the convenience and effectiveness of the proposed AACMM self-calibration approach.
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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 | 0.000 |
| 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 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".