Bibliographic record
Abstract
A brief historic view of design engineering shows the roles of problem definition (design specification), conceptualizing, estimations, layouts, detail and assembly drawings, parts lists, etc., to define an engineering product (process or tangible system according to ISO9000:2005) for implementation and/or manufacture and use. A brief assessment of computer-aided design (CAD) over the last 40 years reveals its detrimental effects on design engineering – the previously usual (intuitive) conceptualizing and preparation of layouts has been neglected. Design engineering is compared to the artistic design disciplines, and differences are highlighted. Design engineering must apply the constraints imposed by the engineering sciences, to satisfy customer require-ments, to consider economics, and to conform to laws and regulations. Yet design engineering offers the opportunity to use several more abstract represen-tations of technical processes and technical systems to aid conceptual designing. A role is indicated for design methods and systematic approaches for design engineering, especially for design and redesign for innovations (non-routine tasks), and in engineering education. Parts of the Theory of Technical Systems and Engineering Design Science are outlined, with conesquences for proposal of a theory-based syste-matic design method. Especially, the role of problem solving as a sub-process in designing shows the need for iterative and recursive procedure. Design enginee-ring demands that sufficient time is available for reflective thought, and needs good time management for any task, especially where innovation is expected. Engineering sciences are shown as necessary, but not sufficient. A broader context needs to include instruc-tion in time management, and should be developed during engineering education in all branches.
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 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 | 0.000 |
| 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".