Early Introduction of Robust Design Into the Engineering Curriculum
Bibliographic record
Abstract
Normally, there is very little opportunity for first-year engineering students to practice robust design techniques given the relatively simple nature of their projects, and they are not exposed to any robust design activity and Design of Experiments (DOE) methodologies until their third year. How can junior engineering students gain a sense of the robustness of their designs? Will the resulting product still be acceptably functional if used in non-ideal environments? The purpose of this paper is to introduce a potential assignment to supplement this need at the first-year level. Introduced as a bonus assignment in Fall 2009, students were charged with the task of designing an aircraft wing by choosing parameter setting combinations that would provide the maximum Lift-to-Drag ratio, simulating results theoretically that would be obtained in a wind-tunnel experiment, while including random noise. All necessary facts and equations were given, leaving students with the task of running calculations and employing Taguchi methods to select an optimal set of parameters. While few students chose to undertake the assignment, those that did it found the application interesting and useful. Example results for this robust design assignment, including final parameter selections for the optimal wing design, are presented in this paper, along with factors where students have shown weaknesses.
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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.032 | 0.016 |
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".