Bibliographic record
Abstract
Continued progress in the area of solid modeler based machining process simulation is hindered by the complexity growth that occurs for a large number of tool paths n. For this reason, many researchers have adopted the Z-buffer approach. Boundary-representation (B-rep), however, remains the dominant choice for commercial modelers. This paper begins by reviewing the current state of solid modeler based machining simulation. Using an industrial example, the growth rate, for a simple feed rate scheduling application, is estimated to be O(n1.5). It is shown that round robin parallel scheduling quickly becomes inefficient due to the fraction of time spent on tool swept volume Boolean subtractions. The tool path sequence is next heuristically subdivided into nearly equal size neighbor groups. Only the Boolean subtractions required for accurate simulation are included in the group. Each group is then simulated in parallel, achieving a greatly reduced wall clock running time. Computational geometry methods are described that permit rapid identification of tool path neighbors. It is shown that, under practical assumptions, the total number of tool path neighbor pairs is O(n), justifying the benefit of parallel processing. Both dual CPU and networked parallel solutions are implemented. Geometric images and running time plots are included to illustrate. Discussion is included, with proposed steps to further reduce calculation time.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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) | 0.008 | 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".