DESIGN, FABRICATION, AND PREDICTIVE MODEL OF A 1-DOF TRANSLATIONAL FLEXIBLE BEARING FOR HIGH PRECISION MECHANISM
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Flexible bearing is significantly associated with high precision manipulators, actuators, and positioning stages. In this paper, a flexible bearing is designed for such applications. The life of a flexible bearing is very sensitively influenced by the stress concentration. The Taguchi method is applied to find the best combination of design variables to reduce the stress concentration. Multivariable linear regression (MLR) is established to model the relationship between the design variables and the stress response. In addition, to enhance the predictive efficiency for predicting, a radial basic function (RBF) neural network is used for this relationship. The effectiveness of all models is compared using statistical methods. It is evident that the relationship derived from RBF neural network is more accurate than that derived from MLR models. The confirmation experiments are conducted to verify the predicted results. The combined methodology in this paper is likely be used for various practical applications.
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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 it