A COMPARISON OF CONCEPT SELECTION IN CONCEPT SCORING AND AXIOMATIC DESIGN METHODS
Bibliographic record
Abstract
The appropriate selection of design concepts has a strong influence on product cost, durability, robustness and functionality. Effective tools to identify good design concepts are critical. Different design methodologies have different objectives to aid in making the right decision. Some design methods are complementary, whereas others may provide contradictory results. In this study, two design methods, concept scoring and axiomatic design, are compared for their ability to obtain good design concepts. Conceptual designs of micro-pumps, which provide pressure gradients to actuate the flow of liquids and gas, are used to evaluate the design methods. Metrics are used to compare these two design methodologies’ abilities to achieve objective evaluations. The concept scoring method is easy to use, especially when comparing different designs. The axiomatic design method provides a structured and mathematical design evaluation; however, achieving good design through the uncoupling or decoupling of matrices is challenging. Further study is required to integrate these two design methods.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".