OPTIMAL MANIPULATOR TOLERANCE DESIGN USING HYBRID EVOLUTIONARY OPTIMIZATION TECHNIQUE
Why this work is in the frame
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Bibliographic record
Abstract
There is a need to select optimal parameter tolerance of manipulator to reach an economic balance between the desired performance and its manufacturing cost. However, selection of optimal parameter tolerances of manipulator is a challenging task. Present paper discusses an offline approach to incorporate effect of noise in simulation of performance and handle its effect in optimization process of parameters tolerances. To determine optimal parameter tolerances, a hybrid evolutionary optimization technique has been used. The hybrid is formed between differential evolution optimization technique and orthogonal array used in design of experiments technique. Proposed technique has been illustrated by selecting optimal tolerances of 2-DOF RR planar manipulator. It has been observed that the methodology is a viable alternative to the costly prototype testing, where only mathematical models are dealt with.
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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