Application of a fuzzy decision support system in a Design for Assembly methodology
Why this work is in the frame
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Bibliographic record
Abstract
Concurrent engineering in product modelling aims at developing a comprehensive practical model capable of driving design, manufacturing, assembly, maintenance and recycling activities. In this paper, an application of fuzzy logic to a Design for Assembly methodology is introduced. The main objective is to compute the assembly efficiency of a product from boundary representation geometric models and a minimal technological database. This work is based on the well-known Boothroyd–Dewhurst methodology for studying manual and automated assemblies. The use of a fuzzy decision support system involves the representation of this method by fuzzy sets. Each part of a product has to undergo computation of its handling and insertion efficiencies, as well as an evaluation of its relevance to the assembly. This evaluation process depends on geometric and technological criteria. The designer may experience difficulties in making a choice when there are several adequate solutions for every part. This paper demonstrates that a decision support system approach significantly improves the Boothroyd–Dewhurst methodology. The proposed approach is flexible and it can be applied to specific products.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 0.000 |
| 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