Comparison of model-based damage imaging techniques for transversely isotropic composites
Bibliographic record
Abstract
In order to reduce operation and maintenance costs of aircraft, in situ structural health monitoring techniques are implemented on critical parts and assemblies. Many of these techniques rely on models considering, with various levels of complexity, the generation, propagation and interaction of ultrasonic guided waves with potential damages, in order to detect, localize and estimate damage severity. Although their potential has been extensively demonstrated on isotropic substrates, their implementation still poses a challenge for composite assemblies for which only quasi-isotropic and cross-ply composites have been considered. This is mainly due to the limitations of the models to properly predict the complex behaviour of guided waves on composites, where the assumptions behind the models actually used for damage imaging do not fully consider the impact of the anisotropy on guided wave generation and propagation. This article presents a comparative analysis of the performances of three model-based damage imaging techniques for composites previously validated on isotropic substrates. The main objective of the study is to address the interest in using more complex analytical formulations to improve the performance of imaging techniques. This is obtained by comparing three imaging techniques, each presenting different levels of complexity in their numerical formulations. Performance of (a) delay-and-sum, (b) dispersion compensation and (c) correlation-based techniques are addressed numerically and experimentally. The analysis is conducted on a unidirectional transversely isotropic laminate instrumented with four circular piezoceramic transducers. A robustness analysis of the models is performed numerically, where the effect of varying stiffness parameters and velocity is addressed. The correlation-based technique is adapted for the first time to composite laminates where the generation is considered using the pin-force model and the propagation is modelled via the use of the global matrix model. Experimental validation is carried out and the results obtained show the benefit of considering the steering effect for well-resolved multi modal damage imaging.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".