Coating position optimization for a hard-coating thin-plate structure based on resonance response
Bibliographic record
Abstract
The ultimate goal of vibration reduction using hard coating is to suppress the resonance peaks of the structure. Thus, the optimal position of the coating as a function of the resonance response will better satisfy the design requirements. Based on a full consideration of the continuous coating area, a method for optimizing the coating position with the objective of minimizing resonance response was developed for the hard-coating thin plate. A semi-analytical analysis model of the partially coated cantilever thin plate was created, and the formula for solving the vibration response was identified by the mode superposition method. An optimization model was established. In the model, the position coordinates of coating patches are the design variables, and the objective function is the maximizing of the reciprocals of the resonance peaks, which is equivalent to minimizing the resonance response. A multi-population genetic algorithm (MPGA) has been proposed to solve the coating position optimization problem. Finally, a cantilever titanium plate coated with NiCrAlCoY + YSZ hard coating was chosen to demonstrate the presented method. The results show that the obtained optimized results can guarantee that the resonance peaks of the hard-coating thin plate are always less than those of general cases, whether it is for single-order or multi-order optimization objectives.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".