Assessment of the excitelet algorithm for in-situ mechanical characterization of orthotropic structures
Bibliographic record
Abstract
Damage detection and localization on composites can be impaired by inaccurate knowledge of the mechanical properties of the structure. This paper demonstrates the feasibility of using a chirplet-based correlation technique, called Excitelet, to evaluate the mechanical properties of orthotropic carbon fibre-based composite laminates. The method relies on the identification of an optimal correlation coefficient between measured and simulated dispersed signals measured on a structure using piezoceramic (PZT) transducers. Finite Element Model (FEM) is first conducted to demonstrate the capability of the approach to evaluate the mechanical properties of a composite structure. Experimental validation is then conducted on a unidirectionnal 2.30 mm thick laminate composed of unidirectional plies and a 2.35 mm thick laminate composed of unidirectional plies oriented at [0, 90]4s. Surface bonded PZT transducers were used both for actuation and sensing of guided waves bursts measured at 0° and 90° with respect to upper ply fibre orientation. The characterization is performed at various frequencies below 100 kHz using A0 or S0 modes and comparison with the material properties measured following ASTM standard testing is presented. The results indicate that large correlation coefficients are obtained between the measurements and simulated signals for both A0 and S0 modes when accurate properties are used as inputs for the model. Strategies based on multiple modes correlation are also assessed in order to improve the accuracy of the characterization approach. The results obtained using the proposed approach for the unidirectional plate and most of the results obtained using the proposed approach for the [0, 90]4s laminate are in agreement with the uncertainty associated with ASTM tests results while the proposed method is non destructive and can be performed prior to each imaging processing.
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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 | 0.002 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".