Complex dynamic stiffness identification of panels using inverse methods based on optical deflectometry measurements
Bibliographic record
Abstract
The Virtual Fields Method and the Force Analysis Technique are two inverse methods that can be applied to identify the bending stiffness normalised by mass per unit area and the loss factor of a Love–Kirchhoff plate. To be applied, both methods require a measured displacement field. This can be measured using optical deflectometry, a full-field measurement technique. However, in optical deflectometry, it is the first-order spatial derivatives of the displacement (also known as slope fields) that are measured and not the displacement directly. This paper proposes new formalisms for the Virtual Fields Method and the Force Analysis Technique so that the methods can be applied using only the slope fields. This process of coupling the two inverse methods with optical deflectometry also involves accurately estimating the spatial step of the experimental mesh. A procedure for measuring this quantity accurately is proposed in this article. The new formalisms are tested and validated with numerical and experimental data, which are used to estimate the bending stiffness normalised by the mass per unit area and the loss factor of a Love–Kirchhoff plate.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".