Optimum First Model Shape Frequency of a New Gripper Employing an Artificial Neural Network
Bibliographic record
Abstract
The gripper mechanism is widely utilized in industry and life.The request for displacement amplifier ratio for this mechanism is high.However, the first modal shape frequency is also required high.Therefore, in this investigation, a novel model of gripper mechanism was designed optimum by employing an artificial neural network (ANN) model.The bridge-type compliant mechanism was applied in the new model mechanism which was drawn by SolidWorks.The first modal shape frequency was determined by finite element analysis (FEA) in ANSYS.The simulated data was used for the analysis of the signal-to-noise, and analysis of variance.The results of the analysis are good and agreed that the design variables have significantly affected on the first modal shape frequency.The statistical analysis outcomes indicated that all the error values are less than 1.The R-square values of training and testing results of ANN model obtained 0.9999 and 1, respectively.The predicted value and the optimal value of the first modal shape frequency obtained 404.6784Hz and 390.72 Hz, respectively.The deviation of the values is 3.45%.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".