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 r4, β, r1 and r5, 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.
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 | 0.002 | 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 | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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 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".