Explainable, Flexible, FRF-Based Parametric Surrogate for Guided Wave-Based Evaluation in Multiple Defect Scenarios


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Date

2025-03-21

Publication Type

Working Paper

ETH Bibliography

yes

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Abstract

Lamb waves offer a series of desirable features for SHM-applications, such as the ability to detect small defects, allowing to detect damage at early stages of its evolution. On the downside, their propagation through media with multiple geometrical features results in complicated patterns, which complicate the task of damage detection, thus hindering the realization of their full potential. This is exacerbated by the fact that numerical models for Lamb waves, which could aid in both the prediction and interpretation of such patterns, are computationally expensive. The present paper provides a flexible surrogate to rapidly evaluate the sensor response in scenarios where Lamb waves propagate in plates that include multiple features or defects. To this end, an offline-online ray tracing approach is combined with FRF and transmissibility functions. Each ray is thereby represented either by a parametrized FRF, if the origin of the ray lies in the actuator, or by a parametrized transmissibility function, if the origin of the ray lies in a feature. By exploiting the mechanical properties of propagating waves, it is possible to minimize the number of training simulations needed for the surrogate, thus avoiding the repeated evaluation of large models. The efficiency of the surrogate is demonstrated numerically, through an example, including different types of features, in particular through holes and notches, which result in both reflection and conversion of incident waves.

Publication status

published

Editor

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Publisher

MDPI

Event

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Subject

Frequency Response Function (FRF); Ultrasonic Guided Waves (UGWs); Structural Health Monitoring (SHM); Reduced Order Model (ROM); Lamb waves

Organisational unit

03890 - Chatzi, Eleni / Chatzi, Eleni check_circle

Notes

Funding

192139 - Fusion of Models and Data for Enriched Evaluation of Structural Health (SNF)

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