COMBINING TAGUCHI METHOD, PRINCIPAL COMPONENT ANALYSIS AND FUZZY LOGIC TO THE TOLERANCE DESIGN OF A DUAL-PURPOSE SIX-BAR MECHANISM
Bibliographic record
Abstract
A hybrid approach of combining Taguchi method, principal component analysis and fuzzy logic for the tolerance design of a dual-purpose six-bar mechanism is proposed. The approach is to firstly use the Taguchi orthogonal array to carry out experiments for calculating the S/N ratios of the positional errors to the angular error of the dual-purpose six-bar mechanism. The principal component analysis is then applied to determine the principal components of the S/N ratios, which are transformed via fuzzy logic reasoning into a multiple performance index (MPI) for further analysis of the effect of each control factors on the quality of the mechanism. Through the analysis of response table and diagram, key dimensional tolerances can be classified, which allows the decision of either to tighten the key tolerances to improve mechanism quality or to relax the tolerance of non-key dimensions to reduce manufacturing costs to be made.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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) | 0.000 | 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".