NURBS representation of estimated surfaces resulting from machining errors
Bibliographic record
Abstract
A new approach to model the actual machining result as a NURBS surface is presented, which explicitly expresses the geometry and topology of the final product and increases the clarity in the mathematical representation of quasistatic machining errors. Most of the available models that estimate interaction of quasistatic machining errors present the actual position of individual machined points and are unable to explicitly describe the resulting machined surface. During the machining process, the desired geometry is mapped from the ideal computer-aided design (CAD) vector space into the machine tool's vector subspaces. Using a Jacobian of the deformed geometry, it is shown that for a variety of three-axis machine tool configurations, a linear operator can be found to express this transformation. Classified error operators for all different configurations of three-axis machine tools are derived and the applicability of the developed method is illustrated by simulating the machining process using case studies. The developed model can be utilised in virtual machining and simulation of the machining process, modification of the design within a design for a manufacturing platform, and also in on-line error compensation during the machining process.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".