Bibliographic record
Abstract
Course ObjectivesThe course conveys a few dominant ideas.First, there is a strong interdependence between design tools and design processes.Introducing new computer-aided design tools in an engineering organization necessarily impacts the engineering processes that support new product development.Students are expected to realize that current CAD tools do no support all phases of the product development process.Therefore, one of the course specific objectives is for students to be able to explain the role of each tool at each product development phase.Second, one major aspect of CAD in aerospace design projects is the size of the organizations involved, where a single aircraft project easily draws on thousands of people creating ideas and sharing information.An adequate infrastructure as well as discipline is needed to efficiently share this evolving product data.Besides, there are as many ways to use CAD tools to document the product description as there are designers using them, some being more efficient than others.Modeling methodologies are used to standardize, to some extent, the methods used to create identified categories of parts.Two major classes of parts are Machined Parts and Sheet Metal Parts.Hence another specific objective of the course is for students to be able to use a Machined Parts Modeling Methodology and a Sheet Metal Part Modeling Methodology.Third, design tools are constantly evolving and many a practicing engineer is bound to decide which ones deserve being implemented to bring benefits in term of cycle time, productivity, quality and cost of product and design processes.It is therefore necessary to be able to evaluate in a structured and rigorous manner the benefits that can be brought to a design organization by the potential introduction of a new design tool.Students are thus expected, as a third specific objective, to be able to design and execute such a systematic evaluation plan.They must also be able to design a complete implementation plan taking into account licenses, legacy data, training as well as financial aspects of a typical software tool implementation project.These objectives are achieved through the study of the course content, described next. Course contentThe educational objectives are achieved by structuring this one-semester course on three poles: formal teaching hours, labs and a project.The following themes are studied during classes, where a unique combination of aerospace knowledge and design tools is proposed.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.032 | 0.011 |
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".