An Object-Space Based Machining Simulation in Milling: Part 2 — By Toroidal Surface
Bibliographic record
Abstract
This is the second part of a two-part paper presenting an efficient parametric approach for updating the in-process workpiece represented by the Z-map. With the Z-map representation, the machining process can be simulated by intersecting z-axis aligned vectors with cutter swept envelopes. In this paper the vector-envelope intersections are formulized for the toroidal section of a fillet-end mill which may be oriented arbitrarily in space. For a given tool motion a toroidal surface generates more than one envelope. In NC machining because the torus is considered as one of the constituent parts of a fillet-end mill, only some parts of the torus envelopes, called contact envelopes, can intersect with Z-map vectors. In this paper an analysis is developed for separating the contact-envelopes from the non-contact ones. When a fillet-end mill has an orientation along the vertical z-axis of the Cartesian coordinate system, which happens in 2 1/2 and 3-axis machining, the number of intersections between a Z-map vector and the swept envelope of a toroidal section of the fillet-end mill is maximum one. For finding this single intersection point one of the numerical root finding methods, i.e. bisection, can be applied to the nonlinear function obtained from vector-envelope intersections. On the other hand when a fillet-end mill has an arbitrary orientation, the number of intersections can be more than one and therefore the numerical root finding methods cannot be applied directly. Therefore for addressing those multiple intersections, a system of non-linear equations in several variables, obtained by intersecting a Z-map vector with the envelope surface of the toroidal section of a fillet-end mill, is transformed into a single variable non-linear function. Then developing a nonlinear root finding analysis which guarantees the root(s) in the given interval, those intersections are obtained.
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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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".