WHICH DESIGN METHODOLOGIES ARE EFFECTIVE TO SUPPORT A CAPSTONE PROJECT IN AEROSPACE DESIGN ENGINEERING?
Bibliographic record
Abstract
Abstract – Considering the challenges in the aerospace industry, the NSERC (Natural Sciences and Engineering Research Council of Canada) Chair in Aerospace Design Engineering (NCADE) has launched its own version of a final year undergraduate engineering capstone project at Concordia University. NCADE’s objective is to expose students to the immense complexity of an aircraft design, thereby better meeting the industry needs of newly graduated students. Four design methodologies (i.e., systems engineering – SE, quality function deployment – QFD, theory of inventive problem solving – TRIZ, and environment-based design – EBD) were evaluated in the context of the NCADE project to answer the research question such as "to what extent do these methodologies provide effective support across the activities in the capstone project?" The evaluation was subjective discussing whether the design methodologies support the activities in the project. From the evaluation, it can be concluded that the studied design methodologies perform poorly to support the activities in the capstone project. Therefore, future research should investigate a better support for the capstone project to achieve NCADE’s goals.
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 | 0.035 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".