A Fuzzy Sustainable Quality Function Deployment Approach to Design for Disassembly with Industry 4.0 Technologies Enablers
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 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.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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