Diagrammatic Visualisation of Early Product Development Information
Bibliographic record
Abstract
There is little methodological support for the early stages of product design that facilitates designers’ cognitive abilities to think about design problems. Yet decisions made during early design are the most crucial to product success. Diagrams present an excellent way to visualize qualitative information, and can stimulate clear, holistic thinking, but there have been no substantive research efforts to apply diagrammatic representation to early engineering design. We therefore introduce design schematics (DS) as such a tool. We outline the general benefits of diagramming and then consider the advantages and disadvantages of some existing diagramming methods. Our analysis motivates the development of DS. Several examples demonstrate how DS can capture important information during early design stages. We are currently developing a computational tool that implements DS and discuss some of the challenges we face in this regard. While there is not yet any quantitative data by which DS can be evaluated, there is anecdotal evidence suggesting that the tool has the potential to be of benefit to practicing designers.Copyright © 2004 by ASME
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.003 |
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".