Developing a Novel Eco-design Approach for Disassembly Based on Fuzzy Sustainable QFD, Customer Segmentation and Circularity
Bibliographic record
Abstract
Abstract Design for Disassembly (DfD) is a challenging concept that facilitates the disassembly of products for refurbishing and reusing their components. In the context of circular economy, DfD minimizes value loss at the end of product’s life and remanufacture costs and maximizes environmental benefits. Therefore, DfD considers technical, environmental financial and social factors, but they are rarely integrated. Today, many studies state that the use of Quality Function Deployment (QFD) approach as a decision support tool helps to make choice by promoting one criterion over one another. However, a systematic approach should also consider uncertainties associated with DfD such as technical features, the recovered parts, the disassembly process, and the optimal disassembly sequence due to the product complexity. The current paper analyzes and compares different QFD approaches in the literature review and then provides a new Fuzzy Sustainable QFD (FS-QFD) methodology, which integrates the three pillars of sustainability. Finally, it shows the effectiveness of the suggested approach through a numerical example.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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 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".