Application of Grey-Taguchi Method Optimizes the Structural Friction Clutch Disc
Bibliographic record
Abstract
In which the clutch disc is the part that is heavily influenced by the pressure disc and engine flywheel.Therefore, the design of the clutch disc faces many difficulties.In this investigation, the model of the clutch disc with design variables and their level was built in SolidWorks.In which, variable A is the friction disc thickness with 3 levels of 3mm, 3.3mm, 3.5mm and B is the diameter of the friction disc groove with levels of 0.25mm, 0.5mm, 0.75mm and variable C is the diameter rivet hole with grades 2.75mm, 3mm, 3.25mm finally D is the young's modulus of the materials for all the part of the clutch are aluminum of 70GPa, copper of 128GPa and structural steel of 200GPa, respectively.The deformation and the equivalent stress were estimated by finite element analysis (FEM) in ANSYS.The simulation data of the study are used to minimize the deformation and stress of the clutch disc by grey relation analysis based on Taguchi method.The results of the FEM indicated that the input variables had a significant influence on the deformation and stress of the clutch disc.Then, the above results were verified by signal to noise (S/N) analysis, analysis of means, analysis of variance, regression analysis, and plot surface.All are in agreement with the error of the predicted value and the optimal value of the grey relational grade is 1.69%.The optimal value of the deformation and stress are 0.033115mm and 66.889MPa, respectively.This proves that these results are very reliable.Therefore, the proposed method is very effective in optimizing the structure of mechanical 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".