Automatic construction of structural CAD models from 3D topology optimization
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
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.
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 | 0.000 | 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