Application of Fuzzy Logic for Optimizing Foldable Freeform Geometries: An example of a practical application – a foldable window shade
Bibliographic record
Abstract
This paper concerns the current discourse on computer-aided design tools for architectural design. There are drawbacks of purely analytic design tools which hinder a system-level, end-effect oriented ideation. For instance, most freeform quadrilateral meshes are fully constrained and therefore not capable of folding. They can only fold under special circumstances – when their geometry satisfi es the conditions of over-constrained kinematics. However, such intent of folding cannot be captured using simple modeling based on parameters and constraints. Furthermore, algorithmization of mesh kinematics using formulas is infl exible, it cannot handle topological variations, and it inhibits the interactive control of the model. In this paper, a fuzzy logic algorithm which uses a goal-oriented, human-like reasoning to control the parametric model is proposed. The algorithm applies easily observable behaviors of the geometry to adjust the selected patches until the entire shell can be folded. The algorithm relies on designer-observable characteristics of motion rather than on formulaic representations. Such approach directs the designers’ focus on the desired outcome while avoiding the drawbacks of analytic modeling of complex kinematics.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".