A Customer-to-Manufacturer Design Model for Custom Compression Casts
Bibliographic record
Abstract
Abstract This paper presents a computational framework for designing and optimizing custom compression casts/braces. Different from the conventional cast/brace design, our framework generates custom casts/braces with fitness, lightweight, and good ventilation. The computational pipeline is an end-to-end solution, directly from customer to the manufacturer, which starts from a 3D scanned human model represented by mesh and ends with the 3D printed cast/brace. Our interactive tools allows users to define and edit the 3D curves on the mesh surface, and trim the mesh surface to form the cast/brace shape using the curves. These tools are efficient and simple to use, and also they enable designing the custom casts/braces fitting to the given human body. In order to reduce the weight and improve the ventilation, we adopt the topology optimization (TO) method to optimize the cast/brace design. We extend the existing three-dimensional (3D) TO method to the mesh surface by simplifying the optimization problem to a 2D problem. Therefore, the efficiency of the TO computation is improved significantly. After the optimized cast/brace design is obtained on the mesh surface, a solid model is generated by our design interface and then sent to a 3D printer for fabrication. Simulation results show that our method can better re-disturb the stresses compared with the conventional 3D TO.
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.019 | 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".