Comparison of Two Machine Learning Models for Predicting Volumetric Errors From On-The-Fly R-Test Type Device Data and Virtual End Point Constraints
Bibliographic record
Abstract
On-the-fly virtual end-point constraints consists in moving all five axes of the machine tool while nominally maintaining the coincidence of a sensing head centre point with a master ball centre attached to the workpiece table. The sensing head detects the deviations from the nominal coincidence as a 3D volumetric error vector. More than one ball can be so measured and a fixed length ball bar is also measured for detecting isotropic scaling effects. Initial processing of data using the SAMBA (scale and master ball artefact) method eliminates setup errors and provides estimates of inter- and intra-axis errors as well as volumetric errors vectors. Two ML models are trained and compared, Neural Network (NN) and eXtreme Gradient Boosting (XGBoost), to find the most suitable model and the required amount of training data to predict volumetric errors of a five-axis machine tool with wCBXfZY(S)t topology based on axis commands. The results show that NN marginally outperforms XGBoost and a kinematic model with ratios of prediction error over volumetric error norms of 0.12, 0.13 and 0.14, respectively.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".