CONCEPTUALIZE–DESIGN ENHANCEMENT OF SYSTEMATIC DESIGN ENGINEERING METHOD
Bibliographic record
Abstract
The engineering design methodology of Pahl and Beitz is good in the detailed stages, but needs enhancement in the early stages of conceptualizing and embodiment-in-principle. The concept of ‘functions’ has been enhanced by Hubka and colleagues. A ‘functional basis’ (Hirtz et al) has improved the definitions of ‘flows’ and ‘functions’, their work does not go far enough to provide a basis for conceptualizing. ‘Affordances’ (Maier and Fadel) are covered by full use of systematic conceptualizing of design engineering solutions. The Pahl-Beitz model and method of ‘decomposition of functions’, ‘physics’, and components is contrasted with the Hubka models of a transformation system, TrfS, its constituents, structures, properties life cycle, etc., and their use as method for design engineering by searching for alternative embodiments at each of these levels of abstraction. These steps are illustrated in (to date) 21 case examples published between 1976 and 2012, several of them in the CEEA conferences and their predecessors.
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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 | 0.041 | 0.040 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".