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 fabrication techniques have become economically prohibitive due to both capital costs and energy inefficiency, as well as high-level artistic and craft skills. 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 based on digital fabrication and hybrid crafts. Based on design thinking and open design principles, this procedure involves fabricating an additional insert made of (i) a PC substrate and (ii) custom geometries directly 3D printed on the substrate with PC-based 3D printing feedstock (iii) to be painted after the 3D printing process. This alternative is intended for customizable stained-glass design patterns to be used instead of traditional stained glass or in addition to conventional windows, making stained glass accessible and customizable according to users’ needs. Three approaches are developed and demonstrated to generate customized painted stained-glass geometries according to the different users’ skills and needs using (i) online-retrieved 3D and 2D patterns; (ii) custom patterns, i.e., hand-drawn and digital-drawn images; and (iii) AI-generated patterns. The proposed methodology shows potential for distributed applications in the building and heritage sectors, demonstrating its practical feasibility. Its use makes stained-glass-based products accessible to a broader range of end-users, especially for repairing and replicating existing conventional stained glass and designing new customizable products. The developed custom patterns are 50 times less expensive than traditional stained glass and can potentially improve thermal insulation, paving the way to energy efficiency and economic savings.
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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 | 0.001 |
| 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".