Multiscale Topology Design Based on Non-Penalisation Smooth-Edged Material Distribution for Optimising Topology (SEMDOT)
Why this work is in the frame
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Bibliographic record
Abstract
This study presents an extension of the Smooth-Edged Material Distribution Optimisation Technique (SEMDOT) to multiscale topology optimisation (MSTO). While the SEMDOT has shown promise in producing smooth and fabrication-friendly structures in various single-scale problems, its application to multiscale design remains unexplored. To extend SEMDOT to MSTO, a discrete sensitivity approach without penalisation is introduced, in which sensitivities are directly determined by classifying elements. Microstructural properties are computed using energy-based homogenisation with periodic boundary conditions (PBCs), enabling efficient and accurate prediction of effective elastic moduli. Physical fidelity of the smooth boundaries estimated by level-set functions are validated. Numerical results from 2D and 3D compliance minimization benchmarks demonstrate the effectiveness of the SEMDOT method, resulting in smooth boundaries between solid and void phases at both macro- and microscales, overcoming the jagged boundaries and grayscale issues seen in conventional methods. The results also show that the SEMDOT achieves comparable performance to other MSTO methods, with fewer iterations and reduced computational time.
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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 it