Digital Topology Optimization Design and Manufacturing Based on the Level Set Method
Bibliographic record
Abstract
This paper presents a unified process of digital structural topological optimization and digital manufacturing. Based on a level set method and an augmented Lagrange multiplier method, the cloud data of the level-set surface for the optimized result are obtained. A new triangular facet approximation method to approach the level-set surface is presented to extract the zero-level-set cloud data of the topologically optimized structure. By using a slice method and the distance formula between two points, the extracted cloud data of the boundaries of the optimized structures are classified into different boundary curves. Along with the standards of the digital manufacturing, the sorted points are translated into a processing program for digital manufacturing of the optimized structures by using an automatic programming technology. The Wire-cut Electrical Discharge Machining is employed to manufacture digitally the work-piece with the optimized design. The digital structural optimization and manufacturing of a 2-D work-piece validate the method and process presented in this paper.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".