A Study on the Recursive Decomposition and Optimization Strategies of N-Face Solids Based on CAD/CAE Integration
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Considering the recurring roadblocks in recursive decomposition of complex solids for CAD/CAE, i.e., the unexpected geometric deviations in initial parameter setting, the fluctuating convergence behavior and the dilemma to seek meshing schemes fulfilling both mesh quality requirement and acceptable computational performance, this paper aims to develop a holistic optimization framework integrating geometric preprocessing, adaptive recursion control strategy, quality-constrained meshing approach and parallelized computational implementation into the decomposition process. Such approach is expected to greatly improve the robustness and computational efficiency of the decomposition procedure and to deliver mesh outputs satisfying both geometric fidelity and engineering simulation requirements as well, which presents a technical solution to achieve high-quality and high-performance mesh generation.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it