Developing a Novel Eco-design Approach for Disassembly Based on Fuzzy Sustainable QFD, Customer Segmentation and Circularity
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 it