Integration of Quality Function Deployment and Functional Analysis for Eco-design
Bibliographic record
Abstract
This paper proposes an eco-design method to systematically generate design concepts for the reduction of environmental impacts. The method is based on the integration of quality function deployment (QFD) and functional analysis (FA) at the early design stage. While QFD provides a framework to reflect the voice of environment in the design planning and evaluations, FA focuses on the functional description of the design to support the generation of design concepts. Particularly, the morphological chart is used to support the synthesis of new design concepts. The integration approach is based on the matrix-based correlation modeling to explicitly capture the links among environmental requirements, engineering metrics, design functions and components. The proposed method consists of four steps. In Step 1, the matrix-based correlation models of the existing design are constructed through QFD and FA. In Step 2, one specific environmental requirement is mapped through the correlation models in order to identify the responsible design functions and components for design generation. Afterwards in Step 3, the identified functions are used to establish the morphological chart to generate possible design solutions (or components) for each function. Then, different design concepts can be synthesized by combining these possible solutions. In Step 4, the generated design concepts can be evaluated via engineering metrics that are relevant to the original environmental requirement. A coffee maker has been selected as an application to demonstrate the proposed 4-step method.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".