Application of a fuzzy decision support system in a Design for Assembly methodology
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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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.002 | 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".