Optiworks: A human-centric framework for high-resolution topology optimization
Bibliographic record
Abstract
Advancements in additive manufacturing have significantly broadened the application of topology optimization. However, the mathematical complexity of these formulations often hinders rapid progress in research and usability. This paper introduces Optiworks, an all-in-one human-centric desktop application that simplifies the entire optimization process from domain initialization to the export of the final optimized design. The software incorporates two key algorithms: the Solid Isotropic Material with Penalization (SIMP) and Smooth-Edged Material Distribution for Optimizing Topology (SEMDOT). Users can select the most suitable method based on their specific needs. Additional features of Optiworks include symmetry enforcement to expedite optimization, static analysis to assess the impact of design changes on total displacement, equivalent strain, and von-mises stress, and the generation of smooth boundaries even with relatively coarse meshes. These capabilities eliminate the need for further post-processing, allowing users to directly export the optimized output as an STL file for immediate 3D printing. Several numerical examples demonstrate the software’s effectiveness.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.040 | 0.010 |
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".