USING THE CASE METHOD TO FACILITATE LEARNING OF DESIGN FOR MANUFACTURING AND COST
Bibliographic record
Abstract
Design for Manufacturing and Assembly (DFMA) is anintegral methodology for product development that aimsto simplify the manufacturing process, increaseproductivity, and minimize costs while maintainingproduct quality. DFMA is often difficult since significantmanufacturing knowledge is required. The importance ofDFMA is underlined by the fact that a large portion ofproduct manufacturing costs (materials, processing,assembly and indirect costs) is determined by early designdecisions. Therefore, it is important for engineeringstudents to understand the limiting factors and practicesrelevant to the application of DFMA. Although DFMAconcepts can be taught through conventional lecturemethods, true understanding of this multi-faceted andhighly integrated strategy requires real-world practice.The case method provides an effective pedagogicalapproach to help students understand and fullyappreciate the complexity of engineering practice, gainexperience and develop the skills necessary to deal withthis complexity, and make connections between varioustopics in their undergraduate curriculum. In this paperwe describe the effort taken in Mechanical Engineering atthe University of Waterloo (UW) to develop andimplement case studies to address this gap.
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 | 0.014 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".