Simulation of XYC trajectory in five-axis machine tool and the Jacobian-based error estimation
Bibliographic record
Abstract
Error estimation methods attract more attention with the increasing demands of five-axis machining. The link and motion errors are an important source of the machine tool deviations. These errors can be identified by the Jacobian matrix, an interesting tool which is simply constructed from the machine tool topology and joint coordinates. In this paper, the volumetric errors are simulated using the homogenous transformation matrix through a circular interpolation of the X and Y axes synchronized with a C-axis rotation. The Jacobian matrix is formed and after a singular value decomposition, the redundant and confounded error parameters are removed and consequently the Jacobian matrix is reduced. The simulated volumetric errors are then used to find error parameters in the case of a horizontal five-axis machine tool. The comparison of simulated and estimated error parameters shows the efficiency of using the Jacobian matrix in error identification.
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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 | 0.001 | 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".