THE USE OF FUZZY LOGIC IN THE TAGUCHI METHOD FOR THE DETERMINATION OF THE TOLERANCE OF A SIX-BAR HINGE MECHANISM
Bibliographic record
Abstract
In this paper fuzzy logic was applied to the Taguchi method to design the dimensional tolerances of a six-bar hinge mechanism with multiple performance characteristics (MPC). A fuzzy logic system was used to determine the relationship between the S/N (signal to noise) ratios of the position and the angle error for assessing the level of importance of each control factor in the hinge mechanism. The contribution of each control factor to the variations was also quantified through the response table and response diagram and the key dimensions found to significantly affect the quality of the mechanism were r 4 , β, r 1 and r 5 , which contributed 67.77% of the total product variation. It followed therefore that in order to improve the quality of the mechanism the tolerance of these factors must be tightened. Through a series of confirmation experiments, it was revealed that tightening the tolerance resulted in an increase in the multiple performance index (MPI) by 0.094, which was an increase of 19.87% of the initial value.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".