Bibliographic record
Abstract
The manufacturing industry is one of the major driving forces for economic success for all industrialized nations. As new technologies are rapidly evolving, the world economy has been undergoing some major changes. The impact of these changes on product manufacturing industry includes more globalized competition, shorter market life cycles, more stringent environmental regulations, more educated customers and so forth. Therefore, products must meet various requirements such as shorter delivery time, customization, environment-friendly, in addition to the traditional requirements of functionality, cost and quality. These requirements cannot be met by simply using more advanced manufacturing technologies only. Manufacturers must produce products according to design. More than 75% of the total product costs as well as all product features are determined at the design stage. Therefore, significant improvement on product quality, features, costs and other life cycle performance can be achieved by improving design. This paper reports a new design method, namely adaptable design, to address those conflict requirements. An adaptable design method has been developed, consisting of four main phases: product modelling, platform design, bus system design and design evaluation and re-design. Examples are provided to illustrate the adaptable design and interface systems. In addition, a review of recent developments in design research is provided. Future research directions and possible topics for research are also discussed for both fundamental as well as applied work in engineering design.
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".