A Case Study of a Systematic Iterative Design Methodology and its Application in Engineering Education
Bibliographic record
Abstract
Iterative design is a design methodology based on a cyclical process of idea generation, evaluation, and design improvement until the design requirement is met.It is a widely used design strategy due to its intuitive nature and effectiveness in facilitating design improvement.As technologies advance rapidly nowadays, the level of complexity of design problems also increases.It is often impossible to develop a good design solution in the first attempt, making the effective application of iterative design strategy even more important.Over the years, researches have been carried out to refine the iterative design strategies and attempts have been made to integrate these design strategies into engineering education.Nevertheless, more efforts should be expended in the field of engineering education to encourage effective use of iterative design strategies by both educators and students.To this end, this paper presents a case study on the development of a plastic foaming visualization system to demonstrate the effectiveness of the axiomatic design approach, which is a systematic iterative design methodology.The methodology is based on two previously established design axioms that were designed to guide idea generation, as well as to streamline analysis and evaluation processes in a product development cycle.In addition, this paper explores possible methods to improve students' learning experiences by integrating iterative design elements in engineering education with regards to course design and assessment/evaluation tools.
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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 | 0.016 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".