OPTIMIZATION-BASED SYNTHESIS OF A DEEP-DIGGING TILLAGE MECHANISM
Bibliographic record
Abstract
A quasi-Newton optimization method is employed to synthesize a four-bar tillage mechanism, a device for loosening the sub-soil in farm fields. The synthesis routine uses sequential parameter transformations that map from an unconstrained search variable space to a constrained design variable space. The sequential parameter transformations ensure that only a specific mechanism sub-type is considered, that Grashof criteria are satisfied, and that all mechanism parameters satisfy specified upper and lower constraints. Objective functions quantifying the level of satisfaction of the desired task displacements are minimized. It is shown that in addition to task satisfaction, further objective function terms can be added. The addition of objective function terms to increase the minimum transmission angle and to reduce the mechanism size are found to allow the synthesis of a practical mechanism for the deep-digging application. The use of the Broyden-Fletcher-Goldfarb-Shanno (BFGS) method with Fletcher’s Line Search (FLS) algorithm has allowed for the development of an efficient optimization-based synthesis routine. The synthesis results demonstrate that the developed synthesis routine required on the order of 102 fewer iterations per start then the direct-search and Sequential Unconstrained Minimization Technique (SUMT) employed in a previous method.
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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) | 0.000 | 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) | 0.002 | 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".