A framework for generative design and drafting for the manufacture and assembly of windows
Bibliographic record
Abstract
The process to fabricate windows for buildings begins with information provided through a web-based tool (also known as a Configure Price Quote), which includes window specifications and geometric information. The fabrication of the windows in the production line consists of the fabrication of the frame, the installation of the glass, the installation of hardware to allow for the operation of the window, the installation of the sealing product, packaging, and shipping to the construction site. This research aims to develop a framework to aid the web-based configuration tool (i.e., Configure Price Quote) to enable the sales representative or client to order and customize windows and improve the flow of information from the Configure Price Quote to the assembly line. As such, the proposed framework aims to automate the design and facilitate the drafting generation for the assembly and fabrication of windows and their sub-components. The proposed framework builds on similar tools developed for the manufacture of other types of products to enable the identification of the material and hardware used to build a window and to assess the business and design rules associated with the type and geometric location of the needed hardware. The research described in this thesis was conducted in collaboration with one of the largest window manufacturers in Canada at their facility in Edmonton, Alberta. The findings of this research are that the proposed framework reduces non-value-added activities in the design phase and improves the flow of information, thereby enabling window manufacturers to provide customized products in an efficient manner. Another notable contribution of this research is its focus on automating the design and facilitating the drafting generation of windows to support assemble-to-order products.
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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.005 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".