Automatic construction of structural CAD models from 3D topology optimization
Bibliographic record
Abstract
Impact of decisions in the design process is initially high and declines as the design matures.However, few computational tools are available for the early design phase, thus an opportunity exists to create such tools.New technology opens up new possibilities to create new and novel computational tools.In this work an existing application is adapted for a new novel 3D input device that is named the Leap Motion controller.The controller allows the user to interact with 3D objects on the screen by using fingers and hands.The of result of this work is a conceptual design application which enables very direct manipulation of 3D objects on the screen, which has not before been achieved for this type of application in 3D.An improved human-computer interaction can potentially improve the users understanding of the structural behavior of a model, cognitive engagement in the design task, and encourage further design exploration.Three different cases are implemented which aims to enable the user to explore different design options with emphasis on geometrical form, as this has the greatest potential to improve the structural performance.The case studies demonstrate new potential for building engineering intuition and improving design space exploration through very direct manipulation in 3D.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".