Design of Alternatives to Stained Glass with Open-source Distributed Additive Manufacturing for Energy Efficiency and Economic Savings
Bibliographic record
Abstract
Stained glass has played important roles in heritage building construction, however, conventional techniques of fabricating it have become economically prohibitive due to both capital costs and energy inefficiency. To overcome these challenges, this study provides a new design methodology for customized 3D printed polycarbonate (PC)-based stained-glass window alternatives using a fully open-source toolchain and methodology. This procedure involves fabricating an additional insert made of (i) a PC substrate and (ii) custom geometries directly 3-D printed on the substrate with PC-based 3D printing feedstock (iii) to be painted after the 3D printing process. This alternative is intended to be used instead of traditional stained glass or, better, in addition to conventional windows to provide stained-glass design patterns while improving thermal insulation. Three approaches are developed and demonstrated in this study to generate customized painted stained-glass designs using (i) online-retrieved 3D and 2D designs; (ii) custom designs, i.e., hand-drawn and digital-drawn images; and (iii) AI-generated designs. The proposed methodology shows potential for distributed and accessible applications in the building and heritage sectors, especially for the repair and replication of existing conventional stained glass and the design of new customizable products that are 50 times less expensive than traditional stained glass.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".