A Fuzzy Sustainable Quality Function Deployment Approach to Design for Disassembly with Industry 4.0 Technologies Enablers
Bibliographic record
Abstract
Abstract Integrating sustainability into product design is a proactive circular economy practice and design for disassembly is an essential eco-design practice for complex product manufacturers. Today, industry 4.0 technologies have considerable influence on product life cycle management, and a few studies address the contributions of these technologies to eco-design methods. Designing the appropriate eco-design tool is challenging considering the complexity of products, organizational instruments, the need for integrating diverse databases, customization of the tool, and incorporating the strategic goals. Hence, a systematic approach is required to address the implications of innovative technologies and integrate the different technical, economic, environmental, and social aspects into the design stage. Quality function deployment (QFD) is an effective approach to integrating customers, technical, and business requirements into new product development. Fuzzy Sustainable QFD is an extended version of this method for considering three pillars of sustainability in design and dealing with qualitative linguistic judgments. This paper proposes a Fuzzy sustainable QFD approach to design for disassembly. A numerical example illustrates the application of the proposed method.
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 | 0.002 | 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) | 0.004 | 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".