A Novel Elasto-Geometric Model Exploiting Loaded Circular Test on a Machine Tool
Bibliographic record
Abstract
Abstract A novel elasto-geometric model is introduced that simultaneously estimates joint compliances and geometric error parameters by employing the loaded double ball bar apparatus. The model parameters are estimated from tests at different force levels by distinguishing between errors that change with the applied force (compliance effect) from those that do not (geometric effects). At lower forces, the geometric errors are dominant whilst at higher forces compliance-induced errors dominate. By feeding the elasto-geometric model with pairs of adjacent force data the evolution of the estimated equivalent local compliance parameters and geometric errors with changes in the applied force are observed. Although theoretically unexpected, the estimated geometric errors also change across the force range. As the force increases the majority of equivalent compliance terms increase such as the dominant equivalent compliances CXXX and CYYY as well as the less significant compliances CXYX and CCCY. As for CCXY and CCYY, no clear trend was observed. Given this observed dependence of the compliance on the force level, the model was enriched by modeling the compliances as linear functions of the applied force. A single set of geometric errors could then be estimated and deemed valid across the load range. The root mean square error (RMSE) value for predicting the radial readings for all force levels for the constant and linearly variable compliance models are 0.0011 and 0.0009 mm, respectively, representing an 18% improvement for the linear compliance model. Both the constant and linearly variable compliance models exhibit over 91% fit to the experimental data with just over 1% improvement for the linear compliance model.
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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 | 0.000 |
| 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 | 0.000 |
| Research integrity | 0.002 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".