Accuracy analysis of a multi-closed-loop deployable mechanism
Bibliographic record
Abstract
The multi-closed-loop deployable mechanism generates distortion which is caused by the deviation of each rod after being assembled as a result of manufacturing error, which leads to the degradation of the antenna performance or excess of the given envelope. The multi-closed-loop deployable mechanisms consist of many closed loops and have complicated topology structure and loop constraints coupling. The positioning accuracy analysis of such mechanism is more difficult than the open-chain mechanisms or the single-loop mechanisms. In order to solve this problem, first, based on the minimum of elastic deformation energy, the distortion analysis model is constructed. Second, the sensitivity of the mechanism distortion to the individual rod deviation is investigated by examining the Lagrange multipliers. By means of this model, the allowable tolerance of multi-closed-loop deployable mechanism is discussed and calculated. Last but not least, the above method and model are applied to the four-closed-loop deployable mechanism to solve the problems of its distortion, sensitivity, and tolerance in both fully deployed and folded configurations. It is demonstrated in this paper that the model and method proposed are well accommodated to the accuracy analysis of such mechanisms. The achievement of the research will help reduce the difficulties of the assembling and manufacturing of the multi-closed-loop deployable mechanisms.
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How this classification was reachedexpand
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".